Split (1)

train · 58.2k rows

datasetId stringlengths 2 117	author stringlengths 2 42 ⌀	last_modified timestamp[us, tz=UTC]	downloads int64 0 9.36M	likes int64 0 3.89k	tags list	task_categories list	createdAt timestamp[us, tz=UTC]	card stringlengths 19 1.01M	embedding list
open-llm-leaderboard/details_marcchew__LaMini-40k-Platypus2-7B	open-llm-leaderboard	2023-12-03T19:17:23Z	310	0	[ "region:us" ]	null	2023-09-18T12:51:35Z	--- pretty_name: Evaluation run of marcchew/LaMini-40k-Platypus2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [marcchew/LaMini-40k-Platypus2-7B](https://huggingface.co/marcchew/LaMini-40k-Platypus2-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_marcchew__LaMini-40k-Platypus2-7B\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-03T19:17:16.064054](https://huggingface.co/datasets/open-llm-leaderboard/details_marcchew__LaMini-40k-Platypus2-7B/blob/main/results_2023-12-03T19-17-16.064054.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.0,\n \"\ acc_stderr\": 0.0\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \ \ \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/marcchew/LaMini-40k-Platypus2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|arc:challenge\|25_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-18T12-51-11.107895.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T08_58_28.638753 path: - '/details_harness\|drop\|3_2023-10-28T08-58-28.638753.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-28T08-58-28.638753.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T08_58_28.638753 path: - '/details_harness\|gsm8k\|5_2023-10-28T08-58-28.638753.parquet' - split: 2023_12_03T19_17_16.064054 path: - '/details_harness\|gsm8k\|5_2023-12-03T19-17-16.064054.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-03T19-17-16.064054.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hellaswag\|10_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T12-51-11.107895.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-18T12-51-11.107895.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_18T12_51_11.107895 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-18T12-51-11.107895.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-18T12-51-11.107895.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T08_58_28.638753 path: - '/details_harness\|winogrande\|5_2023-10-28T08-58-28.638753.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-28T08-58-28.638753.parquet' - config_name: results data_files: - split: 2023_09_18T12_51_11.107895 path: - results_2023-09-18T12-51-11.107895.parquet - split: 2023_10_28T08_58_28.638753 path: - results_2023-10-28T08-58-28.638753.parquet - split: 2023_12_03T19_17_16.064054 path: - results_2023-12-03T19-17-16.064054.parquet - split: latest path: - results_2023-12-03T19-17-16.064054.parquet --- # Dataset Card for Evaluation run of marcchew/LaMini-40k-Platypus2-7B ## Dataset Description - Homepage: - Repository: https://huggingface.co/marcchew/LaMini-40k-Platypus2-7B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [marcchew/LaMini-40k-Platypus2-7B](https://huggingface.co/marcchew/LaMini-40k-Platypus2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_marcchew__LaMini-40k-Platypus2-7B", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-03T19:17:16.064054](https://huggingface.co/datasets/open-llm-leaderboard/details_marcchew__LaMini-40k-Platypus2-7B/blob/main/results_2023-12-03T19-17-16.064054.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### 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]	[ -0.4528172016143799, -0.5204747915267944, 0.2650918662548065, 0.2944256663322449, -0.336825966835022, 0.10287297517061234, -0.42009976506233215, -0.17851434648036957, 0.3669871687889099, 0.7602306008338928, -0.7251136898994446, -0.9090108275413513, -0.6851869225502014, 0.19151003658771515, -0.15304872393608093, 1.1758300065994263, -0.4101884663105011, -0.1166418045759201, -0.030048508197069168, -0.3979426622390747, -0.39713796973228455, -0.4625681936740875, -0.5418952107429504, 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capes	null	2022-11-03T16:15:53Z	309	2	[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "language:pt", "license:unknown", "dissertation-abstracts-translation", "theses-translation", "region:us" ]	[ "translation" ]	2022-03-02T23:29:22Z	--- annotations_creators: - found language_creators: - found language: - en - pt license: - unknown multilinguality: - multilingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: capes pretty_name: CAPES tags: - dissertation-abstracts-translation - theses-translation dataset_info: features: - name: translation dtype: translation: languages: - en - pt config_name: en-pt splits: - name: train num_bytes: 472484364 num_examples: 1157610 download_size: 162229298 dataset_size: 472484364 --- # Dataset Card for CAPES ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage:[Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES](https://sites.google.com/view/felipe-soares/datasets#h.p_kxOR6EhHm2a6) - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil. The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were collected and aligned using the Hunalign algorithm. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### 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 ``` @inproceedings{soares2018parallel, title={A Parallel Corpus of Theses and Dissertations Abstracts}, author={Soares, Felipe and Yamashita, Gabrielli Harumi and Anzanello, Michel Jose}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={345--352}, year={2018}, organization={Springer} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.	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re_dial	null	2022-11-18T21:41:23Z	309	0	[ "task_categories:other", "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "dialogue-sentiment-classification", "region:us" ]	[ "other", "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other - text-classification task_ids: - sentiment-classification paperswithcode_id: redial pretty_name: ReDial (Recommendation Dialogues) tags: - dialogue-sentiment-classification dataset_info: features: - name: movieMentions list: - name: movieId dtype: string - name: movieName dtype: string - name: respondentQuestions list: - name: movieId dtype: string - name: suggested dtype: int32 - name: seen dtype: int32 - name: liked dtype: int32 - name: messages list: - name: timeOffset dtype: int32 - name: text dtype: string - name: senderWorkerId dtype: int32 - name: messageId dtype: int32 - name: conversationId dtype: int32 - name: respondentWorkerId dtype: int32 - name: initiatorWorkerId dtype: int32 - name: initiatorQuestions list: - name: movieId dtype: string - name: suggested dtype: int32 - name: seen dtype: int32 - name: liked dtype: int32 splits: - name: train num_bytes: 13496125 num_examples: 10006 - name: test num_bytes: 1731449 num_examples: 1342 download_size: 5765261 dataset_size: 15227574 --- # Dataset Card for ReDial (Recommendation Dialogues) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [ReDial Dataset](https://redialdata.github.io/website/) - Repository: [ReDialData](https://github.com/ReDialData/website/tree/data) - Paper: [Towards Deep Conversational Recommendations](https://proceedings.neurips.cc/paper/2018/file/800de15c79c8d840f4e78d3af937d4d4-Paper.pdf) - Point of Contact: [ReDial Google Group](https://groups.google.com/forum/embed/?place=forum/redial-dataset&showpopout=true#!forum/redial-dataset) ### Dataset Summary ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users recommend movies to each other. The dataset was collected by a team of researchers working at Polytechnique Montréal, MILA – Quebec AI Institute, Microsoft Research Montréal, HEC Montreal, and Element AI. The dataset allows research at the intersection of goal-directed dialogue systems (such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances JSON-formatted example of a typical instance in the dataset. ``` { "movieMentions":{ "203371":"Final Fantasy: The Spirits Within (2001)", "84779":"The Triplets of Belleville (2003)", "122159":"Mary and Max (2009)", "151313":"A Scanner Darkly (2006)", "191602":"Waking Life (2001)", "165710":"The Boss Baby (2017)" }, "respondentQuestions":{ "203371":{ "suggested":1, "seen":0, "liked":1 }, "84779":{ "suggested":0, "seen":1, "liked":1 }, "122159":{ "suggested":0, "seen":1, "liked":1 }, "151313":{ "suggested":0, "seen":1, "liked":1 }, "191602":{ "suggested":0, "seen":1, "liked":1 }, "165710":{ "suggested":1, "seen":0, "liked":1 } }, "messages":[ { "timeOffset":0, "text":"Hi there, how are you? I'm looking for movie recommendations", "senderWorkerId":0, "messageId":1021 }, { "timeOffset":15, "text":"I am doing okay. What kind of movies do you like?", "senderWorkerId":1, "messageId":1022 }, { "timeOffset":66, "text":"I like animations like @84779 and @191602", "senderWorkerId":0, "messageId":1023 }, { "timeOffset":86, "text":"I also enjoy @122159", "senderWorkerId":0, "messageId":1024 }, { "timeOffset":95, "text":"Anything artistic", "senderWorkerId":0, "messageId":1025 }, { "timeOffset":135, "text":"You might like @165710 that was a good movie.", "senderWorkerId":1, "messageId":1026 }, { "timeOffset":151, "text":"What's it about?", "senderWorkerId":0, "messageId":1027 }, { "timeOffset":207, "text":"It has Alec Baldwin it is about a baby that works for a company and gets adopted it is very funny", "senderWorkerId":1, "messageId":1028 }, { "timeOffset":238, "text":"That seems like a nice comedy", "senderWorkerId":0, "messageId":1029 }, { "timeOffset":272, "text":"Do you have any animated recommendations that are a bit more dramatic? Like @151313 for example", "senderWorkerId":0, "messageId":1030 }, { "timeOffset":327, "text":"I like comedies but I prefer films with a little more depth", "senderWorkerId":0, "messageId":1031 }, { "timeOffset":467, "text":"That is a tough one but I will remember something", "senderWorkerId":1, "messageId":1032 }, { "timeOffset":509, "text":"@203371 was a good one", "senderWorkerId":1, "messageId":1033 }, { "timeOffset":564, "text":"Ooh that seems cool! Thanks for the input. I'm ready to submit if you are.", "senderWorkerId":0, "messageId":1034 }, { "timeOffset":571, "text":"It is animated, sci fi, and has action", "senderWorkerId":1, "messageId":1035 }, { "timeOffset":579, "text":"Glad I could help", "senderWorkerId":1, "messageId":1036 }, { "timeOffset":581, "text":"Nice", "senderWorkerId":0, "messageId":1037 }, { "timeOffset":591, "text":"Take care, cheers!", "senderWorkerId":0, "messageId":1038 }, { "timeOffset":608, "text":"bye", "senderWorkerId":1, "messageId":1039 } ], "conversationId":"391", "respondentWorkerId":1, "initiatorWorkerId":0, "initiatorQuestions":{ "203371":{ "suggested":1, "seen":0, "liked":1 }, "84779":{ "suggested":0, "seen":1, "liked":1 }, "122159":{ "suggested":0, "seen":1, "liked":1 }, "151313":{ "suggested":0, "seen":1, "liked":1 }, "191602":{ "suggested":0, "seen":1, "liked":1 }, "165710":{ "suggested":1, "seen":0, "liked":1 } } } ``` ### Data Fields The dataset is published in the “jsonl” format, i.e., as a text file where each line corresponds to a Dialogue given as a valid JSON document. A Dialogue contains these fields: conversationId: an integer initiatorWorkerId: an integer identifying to the worker initiating the conversation (the recommendation seeker) respondentWorkerId: an integer identifying the worker responding to the initiator (the recommender) messages: a list of Message objects movieMentions: a dict mapping movie IDs mentioned in this dialogue to movie names initiatorQuestions: a dictionary mapping movie IDs to the labels supplied by the initiator. Each label is a bool corresponding to whether the initiator has said he saw the movie, liked it, or suggested it. respondentQuestions: a dictionary mapping movie IDs to the labels supplied by the respondent. Each label is a bool corresponding to whether the initiator has said he saw the movie, liked it, or suggested it. Each Message contains these fields: messageId: a unique ID for this message text: a string with the actual message. The string may contain a token starting with @ followed by an integer. This is a movie ID which can be looked up in the movieMentions field of the Dialogue object. timeOffset: time since start of dialogue in seconds senderWorkerId: the ID of the worker sending the message, either initiatorWorkerId or respondentWorkerId. The labels in initiatorQuestions and respondentQuestions have the following meaning: suggested: 0 if it was mentioned by the seeker, 1 if it was a suggestion from the recommender seen: 0 if the seeker has not seen the movie, 1 if they have seen it, 2 if they did not say liked: 0 if the seeker did not like the movie, 1 if they liked it, 2 if they did not say ### Data Splits The dataset contains a total of 11348 dialogues, 10006 for training and model selection, and 1342 for testing. ## Dataset Creation ### Curation Rationale The dataset allows research at the intersection of goal-directed dialogue systems (such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems. In the dataset, users talk about which movies they like and which ones they do not like, which ones they have seen or not etc., and labels which we ensured agree between the two participants. This allows to research how sentiment is expressed in dialogues, which differs a lot from e.g. review websites. The dialogues and the movies they mention form a curious bi-partite graph structure, which is related to how users talk about the movie (e.g. genre information). Ignoring label information, this dataset can also be viewed as a limited domain chit-chat dialogue dataset. ### Source Data #### Initial Data Collection and Normalization Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process. If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name). If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used. #### Who are the source language producers? Here we formalize the setup of a conversation involving recommendations for the purposes of data collection. To provide some additional structure to our data (and models) we define one person in the dialogue as the recommendation seeker and the other as the recommender. To obtain data in this form, we developed an interface and pairing mechanism mediated by Amazon Mechanical Turk (AMT). We pair up AMT workers and give each of them a role. The movie seeker has to explain what kind of movie he/she likes, and asks for movie suggestions. The recommender tries to understand the seeker’s movie tastes, and recommends movies. All exchanges of information and recommendations are made using natural language. We add additional instructions to improve the data quality and guide the workers to dialogue the way we expect them to. Thus we ask to use formal language and that conversations contain roughly ten messages minimum. We also require that at least four different movies are mentioned in every conversation. Finally, we also ask to converse only about movies, and notably not to mention Mechanical Turk or the task itself. In addition, we ask that every movie mention is tagged using the ‘@’ symbol. When workers type ‘@’, the following characters are used to find matching movie names, and workers can choose a movie from that list. This allows us to detect exactly what movies are mentioned and when. We gathered entities from DBpedia that were of type http://dbpedia.org/ontology/Film to obtain a list of movies, but also allow workers to add their own movies to the list if it is not present already. We obtained the release dates from the movie titles (e.g. http://dbpedia.org/page/American_Beauty_(1999_film), or, if the movie title does not contain that information, from an additional SPARQL request. Note that the year or release date of a movie can be essential to differentiate movies with the same name, but released at different dates. We will refer to these additional labels as movie dialogue forms. Both workers have to answer these forms even though it really concerns the seeker’s movie tastes. Ideally, the two participants would give the same answer to every form, but it is possible that their answers do not coincide (because of carelessness, or dialogue ambiguity). The movie dialogue forms therefore allow us to evaluate sub-components of an overall neural dialogue system more systematically, for example one can train and evaluate a sentiment analysis model directly using these labels. %which could produce a reward for the dialogue agent. In each conversation, the number of movies mentioned varies, so we have different numbers of movie dialogue form answers for each conversation. The distribution of the different classes of the movie dialogue form is shown in Table 1a. The liked/disliked/did not say label is highly imbalanced. This is standard for recommendation data, since people are naturally more likely to talk about movies that they like, and the recommender’s objective is to recommend movies that the seeker is likely to like. ### Annotations #### Annotation process Mentioned in above sub-section. #### Who are the annotators? For the AMT HIT we collect data in English and chose to restrict the data collection to countries where English is the main language. The fact that we pair workers together slows down the data collection since we ask that at least two persons are online at the same time to do the task, so a good amount of workers is required to make the collection possible. Meanwhile, the task is quite demanding, and we have to select qualified workers. HIT reward and qualification requirement were decisive to get good conversation quality while still ensuring that people could get paired together. We launched preliminary HITs to find a compromise and finally set the reward to $0.50 per person for each completed conversation (so each conversation costs us $1, plus taxes), and ask that workers meet the following requirements: (1)~Approval percentage greater than 95, (2)~Number of approved HITs greater than 1000, (3)~Their location must be in United States, Canada, United Kingdom, Australia, or New Zealand. ### Personal and Sensitive Information Workers had to confirm a consent form before every task that explains what the data is being collected for and how it is going to be used. ## 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 The dataset collection was funded by Google, IBM, and NSERC, with editorial support from Microsoft Research. ### Licensing Information The data is published under the CC BY 4.0 License. ### Citation Information ``` @inproceedings{li2018conversational, title={Towards Deep Conversational Recommendations}, author={Li, Raymond and Kahou, Samira Ebrahimi and Schulz, Hannes and Michalski, Vincent and Charlin, Laurent and Pal, Chris}, booktitle={Advances in Neural Information Processing Systems 31 (NIPS 2018)}, year={2018} } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.	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shmuhammad/AfriSenti-twitter-sentiment	shmuhammad	2023-09-03T09:59:15Z	309	3	[ "task_categories:text-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "task_ids:semantic-similarity-classification", "task_ids:semantic-similarity-scoring", "multilinguality:monolingual", "multilinguality:multilingual", "size_categories:100K<n<1M", "language:amh", "language:ary", "language:ar", "language:arq", "language:hau", "language:ibo", "language:kin", "language:por", "language:pcm", "language:eng", "language:oro", "language:swa", "language:tir", "language:twi", "language:tso", "language:yor", "sentiment analysis, Twitter, tweets", "sentiment", "arxiv:2302.08956", "arxiv:2304.06845", "arxiv:2201.08277", "region:us" ]	[ "text-classification" ]	2023-02-16T21:02:20Z	--- task_categories: - text-classification task_ids: - sentiment-analysis - sentiment-classification - sentiment-scoring - semantic-similarity-classification - semantic-similarity-scoring tags: - sentiment analysis, Twitter, tweets - sentiment multilinguality: - monolingual - multilingual size_categories: - 100K<n<1M language: - amh - ary - ar - arq - hau - ibo - kin - por - pcm - eng - oro - swa - tir - twi - tso - yor pretty_name: AfriSenti --- # Dataset Card for AfriSenti Dataset <p align="center"> <img src="https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/images/afrisenti-twitter.png", width="700" height="500"> -------------------------------------------------------------------------------- ## Dataset Description - Homepage: https://github.com/afrisenti-semeval/afrisent-semeval-2023 - Repository: [GitHub](https://github.com/afrisenti-semeval/afrisent-semeval-2023) - Paper: [AfriSenti: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://arxiv.org/pdf/2302.08956.pdf) - Paper: [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://arxiv.org/pdf/2304.06845.pdf) - Paper: [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://arxiv.org/pdf/2201.08277.pdf) - Leaderboard: N/A - Point of Contact: [shamsuddeen Muhammad]([email protected]) ### Dataset Summary AfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba). The datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages. ### Supported Tasks and Leaderboards The AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages. [SemEval 2023 Task 12 : Sentiment Analysis for African Languages](https://codalab.lisn.upsaclay.fr/competitions/7320) ### Languages 14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)). ## Dataset Structure ### Data Instances For each instance, there is a string for the tweet and a string for the label. See the AfriSenti [dataset viewer](https://huggingface.co/datasets/shmuhammad/AfriSenti/viewer/shmuhammad--AfriSenti/train) to explore more examples. ``` { "tweet": "string", "label": "string" } ``` ### Data Fields The data fields are: ``` tweet: a string feature. label: a classification label, with possible values including positive, negative and neutral. ``` ### Data Splits The AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset. \| \| ama \| arq \| hau \| ibo \| ary \| orm \| pcm \| pt-MZ \| kin \| swa \| tir \| tso \| twi \| yo \| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| train \| 5,982 \| 1,652 \| 14,173 \| 10,193 \| 5,584\| - \| 5,122 \| 3,064 \| 3,303 \| 1,811 \| - \| 805 \| 3,482\| 8,523 \| \| dev \| 1,498 \| 415 \| 2,678 \| 1,842 \| 1,216 \| 397 \| 1,282 \| 768 \| 828 \| 454 \| 399 \| 204 \| 389 \| 2,091 \| \| test \| 2,000 \| 959 \| 5,304 \| 3,683 \| 2,962 \| 2,097 \| 4,155 \| 3,663 \| 1,027 \| 749 \| 2,001 \| 255 \| 950 \| 4,516 \| \| total \| 9,483 \| 3,062 \| 22,155 \| 15,718 \| 9,762 \| 2,494 \| 10,559 \| 7,495 \| 5,158 \| 3,014 \| 2,400 \| 1,264 \| 4,821 \| 15,130 \| ### How to use it ```python from datasets import load_dataset # you can load specific languages (e.g., Amharic). This download train, validation and test sets. ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh") # train set only ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "train") # test set only ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "test") # validation set only ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "validation") ``` ## Dataset Creation ### Curation Rationale AfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task [SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval)](https://afrisenti-semeval.github.io). ### Source Data Twitter #### 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 We anonymized the tweets by replacing all @mentions by @user and removed all URLs. ## Considerations for Using the Data ### Social Impact of Dataset The Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies. [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators AfriSenti is an extension of NaijaSenti, a dataset consisting of four Nigerian languages: Hausa, Yoruba, Igbo, and Nigerian-Pidgin. This dataset has been expanded to include other 10 African languages, and was curated with the help of the following: \| Language \| Dataset Curators \| \|---\|---\| \| Algerian Arabic (arq) \| Nedjma Ousidhoum, Meriem Beloucif \| \| Amharic (ama) \| Abinew Ali Ayele, Seid Muhie Yimam \| \| Hausa (hau) \| Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello \| \| Igbo (ibo) \| Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello \| \| Kinyarwanda (kin)\| Samuel Rutunda \| \| Moroccan Arabic/Darija (ary) \| Oumaima Hourrane \| \| Mozambique Portuguese (pt-MZ) \| Felermino Dário Mário António Ali \| \| Nigerian Pidgin (pcm) \| Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello \| \| Oromo (orm) \| Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay \| \| Swahili (swa) \| Davis Davis \| \| Tigrinya (tir) \| Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay \| \| Twi (twi) \| Salomey Osei, Bernard Opoku, Steven Arthur \| \| Xithonga (tso) \| Felermino Dário Mário António Ali \| \| Yoruba (yor) \| Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello \| ### Licensing Information This AfriSenti is licensed under a Creative Commons Attribution 4.0 International License ### Citation Information ``` @inproceedings{Muhammad2023AfriSentiAT, title={AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages}, author={Shamsuddeen Hassan Muhammad and Idris Abdulmumin and Abinew Ali Ayele and Nedjma Ousidhoum and David Ifeoluwa Adelani and Seid Muhie Yimam and Ibrahim Sa'id Ahmad and Meriem Beloucif and Saif Mohammad and Sebastian Ruder and Oumaima Hourrane and Pavel Brazdil and Felermino D'ario M'ario Ant'onio Ali and Davis Davis and Salomey Osei and Bello Shehu Bello and Falalu Ibrahim and Tajuddeen Gwadabe and Samuel Rutunda and Tadesse Belay and Wendimu Baye Messelle and Hailu Beshada Balcha and Sisay Adugna Chala and Hagos Tesfahun Gebremichael and Bernard Opoku and Steven Arthur}, year={2023} } ``` ``` @article{muhammad2023semeval, title={SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)}, author={Muhammad, Shamsuddeen Hassan and Abdulmumin, Idris and Yimam, Seid Muhie and Adelani, David Ifeoluwa and Ahmad, Ibrahim Sa'id and Ousidhoum, Nedjma and Ayele, Abinew and Mohammad, Saif M and Beloucif, Meriem}, journal={arXiv preprint arXiv:2304.06845}, year={2023} } ``` ### Contributions [More Information Needed]	[ -0.721317708492279, -0.42559051513671875, -0.15938173234462738, 0.574791669845581, -0.2674374580383301, -0.08619587123394012, -0.35189560055732727, -0.4482112526893616, 0.7616528868675232, 0.21059823036193848, -0.5897042155265808, -0.8027182817459106, -0.7694768309593201, 0.26498472690582275, -0.23570287227630615, 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mstz/adult	mstz	2023-04-15T11:37:47Z	309	1	[ "task_categories:tabular-classification", "size_categories:10K<n<100K", "language:en", "license:cc", "adult", "tabular_classification", "binary_classification", "multiclass_classification", "UCI", "region:us" ]	[ "tabular-classification" ]	2023-02-27T21:17:48Z	--- language: - en tags: - adult - tabular_classification - binary_classification - multiclass_classification - UCI pretty_name: Adult size_categories: - 10K<n<100K task_categories: - tabular-classification configs: - encoding - income - income-no race - race license: cc --- # Adult The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets). Census dataset including personal characteristic of a person, and their income threshold. # Configurations and tasks \| Configuration \| Task \| Description \| \|-------------------\|---------------------------\|-----------------------------------------------------------------\| \| encoding \| \| Encoding dictionary showing original values of encoded features.\| \| income \| Binary classification \| Classify the person's income as over or under the threshold. \| \| income-no race \| Binary classification \| As `income`, but the `race` feature is removed. \| \| race \| Multiclass classification \| Predict the race of the individual. \| # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/adult", "income")["train"] ``` # Features Target feature changes according to the selected configuration and is always in last position in the dataset. \|Feature \|Type \| Description \| \|-------------------------------\|-----------\|------------------------------------------------------------\| \|`age` \|`[int64]` \| Age of the person. \| \|`capital_gain` \|`[float64]`\| Capital gained by the person. \| \|`capital_loss` \|`[float64]`\| Capital lost by the person. \| \|`education` \|`[int8]` \| Education level: the higher, the more educated the person. \| \|`final_weight` \|`[int64]` \| \| \|`hours_worked_per_week` \|`[int64]` \| Hours worked per week. \| \|`marital_status` \|`[string]` \| Marital status of the person. \| \|`native_country` \|`[string]` \| Native country of the person. \| \|`occupation` \|`[string]` \| Job of the person. \| \|`race` \|`[string]` \| Race of the person. \| \|`relationship` \|`[string]` \| \| \|`is_male` \|`[bool]` \| Man/Woman. \| \|`workclass` \|`[string]` \| Type of job of the person. \| \|over_threshold \|`int8` \| `1` for income `>= 50k$`, `0` otherwise. \|	[ -0.2252243459224701, -0.33637845516204834, 0.11291195452213287, 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jxu124/refcocog	jxu124	2023-05-20T19:00:12Z	309	1	[ "region:us" ]	null	2023-04-26T12:00:59Z	--- dataset_info: features: - name: image_id dtype: int64 - name: split dtype: string - name: sentences list: - name: raw dtype: string - name: sent dtype: string - name: sent_id dtype: int64 - name: tokens sequence: string - name: file_name dtype: string - name: category_id dtype: int64 - name: ann_id dtype: int64 - name: sent_ids sequence: int64 - name: ref_id dtype: int64 - name: raw_anns dtype: string - name: raw_image_info dtype: string - name: raw_sentences dtype: string - name: image_path dtype: string - name: bbox sequence: float64 - name: captions sequence: string - name: global_image_id dtype: string - name: anns_id dtype: string splits: - name: test num_bytes: 10341980 num_examples: 5023 - name: train num_bytes: 87352599 num_examples: 42226 - name: validation num_bytes: 5236723 num_examples: 2573 download_size: 45968855 dataset_size: 102931302 --- # Dataset Card for "refcocog" [More 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emi429/humansleepproject-rr-pretraining	emi429	2023-11-08T18:46:04Z	309	0	[ "region:us" ]	null	2023-11-06T19:48:00Z	--- dataset_info: features: - name: rr_intervals sequence: float64 - name: patient_id dtype: string splits: - name: train num_bytes: 119094553 num_examples: 469 download_size: 19848443 dataset_size: 119094553 --- # Dataset Card for "humansleepproject-rr-pretraining" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.4614735245704651, 0.0263755414634943, 0.05227375030517578, 0.3260539770126343, -0.18108351528644562, 0.16850274801254272, 0.2030727118253708, -0.2602892518043518, 0.8122130632400513, 0.3239082396030426, -1.07997465133667, -0.5923617482185364, -0.49190253019332886, -0.22284093499183655, -0.1681765764951706, 1.2563387155532837, -0.035789769142866135, 0.1500129997730255, -0.37807387113571167, -0.002381819998845458, -0.39191240072250366, -0.7982200384140015, -0.4505801498889923, 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kor_3i4k	null	2023-01-25T14:33:43Z	308	1	[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ko", "license:cc-by-4.0", "arxiv:1811.04231", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ko license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification pretty_name: 3i4K dataset_info: features: - name: label dtype: class_label: names: '0': fragment '1': statement '2': question '3': command '4': rhetorical question '5': rhetorical command '6': intonation-dependent utterance - name: text dtype: string splits: - name: train num_bytes: 3102158 num_examples: 55134 - name: test num_bytes: 344028 num_examples: 6121 download_size: 2956114 dataset_size: 3446186 --- # Dataset Card for 3i4K ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [3i4K](https://github.com/warnikchow/3i4k) - Repository: [3i4K](https://github.com/warnikchow/3i4k) - Paper: [Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency](https://arxiv.org/abs/1811.04231) - Point of Contact: [Won Ik Cho]([email protected]) ### Dataset Summary The 3i4K dataset is a set of frequently used Korean words (corpus provided by the Seoul National University Speech Language Processing Lab) and manually created questions/commands containing short utterances. The goal is to identify the speaker intention of a spoken utterance based on its transcript, and whether in some cases, requires using auxiliary acoustic features. The classification system decides whether the utterance is a fragment, statement, question, command, rhetorical question, rhetorical command, or an intonation-dependent utterance. This is important because in head-final languages like Korean, the level of the intonation plays a significant role in identifying the speaker's intention. ### Supported Tasks and Leaderboards * `intent-classification`: The dataset can be trained with a CNN or BiLISTM-Att to identify the intent of a spoken utterance in Korean and the performance can be measured by its F1 score. ### Languages The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`. ## Dataset Structure ### Data Instances An example data instance contains a short utterance and it's label: ``` { "label": 3, "text": "선수잖아 이 케이스 저 케이스 많을 거 아냐 선배라고 뭐 하나 인생에 도움도 안주는데 내가 이렇게 진지하게 나올 때 제대로 한번 조언 좀 해줘보지" } ``` ### Data Fields * `label`: determines the intention of the utterance and can be one of `fragment` (0), `statement` (1), `question` (2), `command` (3), `rhetorical question` (4), `rhetorical command` (5) and `intonation-depedent utterance` (6). * `text`: the text in Korean about common topics like housework, weather, transportation, etc. ### Data Splits The data is split into a training set comrpised of 55134 examples and a test set of 6121 examples. ## Dataset Creation ### Curation Rationale For head-final languages like Korean, intonation can be a determining factor in identifying the speaker's intention. The purpose of this dataset is to to determine whether an utterance is a fragment, statement, question, command, or a rhetorical question/command using the intonation-depedency from the head-finality. This is expected to improve language understanding of spoken Korean utterances and can be beneficial for speech-to-text applications. ### Source Data #### Initial Data Collection and Normalization The corpus was provided by Seoul National University Speech Language Processing Lab, a set of frequently used words from the National Institute of Korean Language and manually created commands and questions. The utterances cover topics like weather, transportation and stocks. 20k lines were randomly selected. #### Who are the source language producers? Korean speakers produced the commands and questions. ### Annotations #### Annotation process Utterances were classified into seven categories. They were provided clear instructions on the annotation guidelines (see [here](https://docs.google.com/document/d/1-dPL5MfsxLbWs7vfwczTKgBq_1DX9u1wxOgOPn1tOss/edit#) for the guidelines) and the resulting inter-annotator agreement was 0.85 and the final decision was done by majority voting. #### Who are the annotators? The annotation was completed by three Seoul Korean L1 speakers. ### 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 The dataset is curated by Won Ik Cho, Hyeon Seung Lee, Ji Won Yoon, Seok Min Kim and Nam Soo Kim. ### Licensing Information The dataset is licensed under the CC BY-SA-4.0. ### Citation Information ``` @article{cho2018speech, title={Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency}, author={Cho, Won Ik and Lee, Hyeon Seung and Yoon, Ji Won and Kim, Seok Min and Kim, Nam Soo}, journal={arXiv preprint arXiv:1811.04231}, year={2018} } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.	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oclar	null	2022-11-03T16:15:26Z	308	1	[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "license:unknown", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-classification - sentiment-scoring paperswithcode_id: null pretty_name: OCLAR dataset_info: features: - name: pagename dtype: string - name: review dtype: string - name: rating dtype: int8 splits: - name: train num_bytes: 398204 num_examples: 3916 download_size: 382976 dataset_size: 398204 --- # Dataset Card for OCLAR ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [OCLAR homepage](http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#) - Paper: [paper link](https://www.semanticscholar.org/paper/Sentiment-Classifier%3A-Logistic-Regression-for-in-Omari-Al-Hajj/9319f4d9e8b3b7bfd0d214314911c071ba7ce1a0) - Point of Contact: [Marwan Al Omari]([email protected]) ### Dataset Summary The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews [Zomato website](https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc. The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451 texts. ### Supported Tasks and Leaderboards Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services reviews, including hotels, restaurants, shops, and others. ### Languages The text in the dataset is in Arabic, mainly in Lebanese (LB). The associated BCP-47 code is `ar-LB`. ## Dataset Structure ### Data Instances A typical data point comprises a `pagename` which is the name of service / location being reviewed, a `review` which is the review left by the user / client , and a `rating` which is a score between 1 and 5. The authors consider a review to be positive if the score is greater or equal than `3`, else it is considered negative. An example from the OCLAR data set looks as follows: ``` "pagename": 'Ramlet Al Baida Beirut Lebanon', "review": 'مكان يطير العقل ويساعد على الاسترخاء', "rating": 5, ``` ### Data Fields - `pagename`: string name of the service / location being reviewed - `review`: string review left by the user / costumer - `rating`: number of stars left by the reviewer. It ranges from 1 to 5. ### Data Splits The data set comes in a single csv file of a total `3916` reviews : - `3465` are considered positive (a rating of 3 to 5) - `451` are considered negative (a rating of 1 or 2) ## Dataset Creation ### Curation Rationale This dataset was created for Arabic sentiment classification on services’ reviews in Lebanon country. Reviews are about public services, including hotels, restaurants, shops, and others. ### Source Data #### Initial Data Collection and Normalization The data was collected from Google Reviews and [Zomato website](https://www.zomato.com/lebanon) #### Who are the source language producers? The source language producers are people who posted their reviews on Google Reviews or [Zomato website](https://www.zomato.com/lebanon). They're mainly Arabic speaking Lebanese people. ### Annotations #### Annotation process The dataset does not contain any additional annotations #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The author's research has tackled a highly important task of sentiment analysis for Arabic language in the Lebanese context on 3916 reviews’ services from Google and Zomato. Experiments show three main findings: 1) The classifier is confident when used to predict positive reviews, 2) while it is biased on predicting reviews with negative sentiment, and finally 3) the low percentage of negative reviews in the corpus contributes to the diffidence of LR. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by Marwan Al Omari, Moustafa Al-Hajj from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon; Nacereddine Hammami from college of Computer and Information Sciences, Jouf University, Aljouf, KSA; and Amani Sabra from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon. ### Licensing Information [More Information Needed] ### Citation Information - Marwan Al Omari, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, marwanalomari '@' yahoo.com - Moustafa Al-Hajj, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, moustafa.alhajj '@' ul.edu.lb - Nacereddine Hammami, college of Computer and Information Sciences, Jouf University, Aljouf, KSA, n.hammami '@' ju.edu.sa - Amani Sabra, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, amani.sabra '@' ul.edu.lb ``` @misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @InProceedings{AlOmari2019oclar, title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon}, authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.}, year={2019} } ``` ### Contributions Thanks to [@alaameloh](https://github.com/alaameloh) for adding this dataset.	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taskmaster3	null	2022-11-03T16:30:39Z	308	0	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:1909.05358", "region:us" ]	[ "text-generation", "fill-mask" ]	2022-03-02T23:29:22Z	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: null pretty_name: taskmaster3 dataset_info: features: - name: conversation_id dtype: string - name: vertical dtype: string - name: instructions dtype: string - name: scenario dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: apis list: - name: name dtype: string - name: index dtype: int32 - name: args list: - name: arg_name dtype: string - name: arg_value dtype: string - name: response list: - name: response_name dtype: string - name: response_value dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 143609327 num_examples: 23757 download_size: 313402141 dataset_size: 143609327 --- # Dataset Card for taskmaster3 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Taskmaster](https://research.google/tools/datasets/taskmaster-1/) - Repository: [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020) - Paper: [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358) - Leaderboard: N/A - Point of Contact: [Taskmaster Googlegroup]([email protected]) ### Dataset Summary Taskmaster is dataset for goal oriented conversations. The Taskmaster-3 dataset consists of 23,757 movie ticketing dialogs. By "movie ticketing" we mean conversations where the customer's goal is to purchase tickets after deciding on theater, time, movie name, number of tickets, and date, or opt out of the transaction. This collection was created using the "self-dialog" method. This means a single, crowd-sourced worker is paid to create a conversation writing turns for both speakers, i.e. the customer and the ticketing agent. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in English language. ## Dataset Structure ### Data Instances A typical example looks like this ``` { "conversation_id": "dlg-ddee80da-9ffa-4773-9ce7-f73f727cb79c", "instructions": "SCENARIO: Pretend you’re using a digital assistant to purchase tickets for a movie currently showing in theaters. ...", "scenario": "4 exchanges with 1 error and predefined variables", "utterances": [ { "apis": [], "index": 0, "segments": [ { "annotations": [ { "name": "num.tickets" } ], "end_index": 21, "start_index": 20, "text": "2" }, { "annotations": [ { "name": "name.movie" } ], "end_index": 42, "start_index": 37, "text": "Mulan" } ], "speaker": "user", "text": "I would like to buy 2 tickets to see Mulan." }, { "index": 6, "segments": [], "speaker": "user", "text": "Yes.", "apis": [ { "args": [ { "arg_name": "name.movie", "arg_value": "Mulan" }, { "arg_name": "name.theater", "arg_value": "Mountain AMC 16" } ], "index": 6, "name": "book_tickets", "response": [ { "response_name": "status", "response_value": "success" } ] } ] } ], "vertical": "Movie Tickets" } ``` ### Data Fields Each conversation in the data file has the following structure: - `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning. - `utterances`: A list of utterances that make up the conversation. - `instructions`: Instructions for the crowdsourced worker used in creating the conversation. - `vertical`: In this dataset the vertical for all dialogs is "Movie Tickets". - `scenario`: This is the title of the instructions for each dialog. Each utterance has the following fields: - `index`: A 0-based index indicating the order of the utterances in the conversation. - `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance. - `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers. - `segments`: A list of various text spans with semantic annotations. - `apis`: An array of API invocations made during the utterance. Each API has the following structure: - `name`: The name of the API invoked (e.g. find_movies). - `index`: The index of the parent utterance. - `args`: A `list` of `dict` with keys `arg_name` and `arg_value` which represent the name of the argument and the value for the argument respectively. - `response`: A `list` of `dict`s with keys `response_name` and `response_value` which represent the name of the response and the value for the response respectively. Each segment has the following fields: - `start_index`: The position of the start of the annotation in the utterance text. - `end_index`: The position of the end of the annotation in the utterance text. - `text`: The raw text that has been annotated. - `annotations`: A list of annotation details for this segment. Each annotation has a single field: - `name`: The annotation name. ### Data Splits There are no deafults splits for all the config. The below table lists the number of examples in each config. \| \| Train \| \|-------------------\|--------\| \| n_instances \| 23757 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is licensed under `Creative Commons Attribution 4.0 License` ### Citation Information [More Information Needed] ``` @inproceedings{48484, title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset}, author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, year = {2019} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.	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MoritzLaurer/multilingual-NLI-26lang-2mil7	MoritzLaurer	2022-08-22T21:40:14Z	308	28	[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "annotations_creators:crowdsourced", "language_creators:machinetranslation", "size_categories:1M<n<5", "source_datasets:multi_nli", "source_datasets:anli", "source_datasets:fever", "source_datasets:lingnli", "source_datasets:alisawuffles/WANLI", "language:multilingual", "language:zh", "language:ja", "language:ar", "language:ko", "language:de", "language:fr", "language:es", "language:pt", "language:hi", "language:id", "language:it", "language:tr", "language:ru", "language:bn", "language:ur", "language:mr", "language:ta", "language:vi", "language:fa", "language:pl", "language:uk", "language:nl", "language:sv", "language:he", "language:sw", "language:ps", "arxiv:2104.07179", "region:us" ]	[ "text-classification" ]	2022-08-17T15:28:16Z	--- annotations_creators: - crowdsourced language_creators: - machinetranslation size_categories: - 1M<n<5 source_datasets: - multi_nli - anli - fever - lingnli - alisawuffles/WANLI task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification language: - multilingual - zh - ja - ar - ko - de - fr - es - pt - hi - id - it - tr - ru - bn - ur - mr - ta - vi - fa - pl - uk - nl - sv - he - sw - ps --- # Datasheet for the dataset: multilingual-NLI-26lang-2mil7 ## Dataset Summary This dataset contains 2 730 000 NLI text pairs in 26 languages spoken by more than 4 billion people. The dataset can be used to train models for multilingual NLI (Natural Language Inference) or zero-shot classification. The dataset is based on the English datasets [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [ANLI](https://huggingface.co/datasets/anli), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) and was created using the latest open-source machine translation models. The dataset is designed to complement the established multilingual [XNLI](https://huggingface.co/datasets/xnli) dataset. XNLI contains older machine translations of the MultiNLI dataset from 2018 for 14 languages, as well as human translations of 2490 texts for validation and 5010 texts for testing per language. multilingual-NLI-26lang-2mil7 is sourced from 5 different NLI datasets and contains 105 000 machine translated texts for each of 26 languages, leading to 2 730 000 NLI text pairs. The release of the dataset is accompanied by the fine-tuned [mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) model, which can be used for NLI or zero-shot classification in 100 languages. ## Dataset Creation The languages in the dataset are: ['ar', 'bn', 'de', 'es', 'fa', 'fr', 'he', 'hi', 'id', 'it', 'ja', 'ko', 'mr', 'nl', 'pl', 'ps', 'pt', 'ru', 'sv', 'sw', 'ta', 'tr', 'uk', 'ur', 'vi', 'zh'] (see [ISO language codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)) plus the original English texts. The languages were chosen based on two criteria: (1) They are either included in the list of the [20 most spoken languages](https://en.wikipedia.org/wiki/List_of_languages_by_total_number_of_speakers) (excluding Telugu and Nigerian Pidgin, for which no machine translation model was available); (2) or they are spoken in polit-economically important countries such as the [G20](https://en.wikipedia.org/wiki/G20) or Iran and Israel. For each of the 26 languages, a different random sample of 25 000 hypothesis-premise pairs was taken from each of the following four datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli) (392 702 texts in total), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md) (196 805 texts), [ANLI](https://huggingface.co/datasets/anli) (162 865 texts), [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) (102 885 texts). Moreover, a sample of 5000 texts was taken from [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) (29 985 texts) given its smaller total size. This leads to a different random sample of 105 000 source texts per target language with a diverse distribution of data from 5 different NLI datasets. Each sample was then machine translated using the latest open-source machine translation models available for the respective language: - [opus-mt-tc-big models](https://huggingface.co/models?sort=downloads&search=opus-mt-tc-big) were available for English to ['ar', 'es', 'fr', 'it', 'pt', 'tr'] - [opus-mt-models](https://huggingface.co/models?sort=downloads&search=opus-mt) were available for English to ['de', 'he', 'hi', 'id', 'mr', 'nl', 'ru', 'sv', 'sw', 'uk', 'ur', 'vi', 'zh'] - [m2m100_1.2B](https://huggingface.co/facebook/m2m100_1.2B) was used for the remaining languages ['bn', 'fa', 'ja', 'ko', 'pl', 'ps', 'ta'] ## DatasetStructure ### Data Splits The dataset contains 130 splits (26 * 5), one for each language-dataset pair following the format '{language-iso}_{dataset}'. For example, split 'zh_mnli' contains the Chinese translation of 25 000 texts from the MultiNLI dataset etc. ### Data Fields - `premise_original`: The original premise from the English source dataset - `hypothesis_original`: The original hypothesis from the English source dataset - `label`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2). - `premise`: The machine translated premise in the target language - `hypothesis`: The machine translated premise in the target language ### Example of a data instance: ``` { "premise_original": "I would not be surprised if the top priority for the Navy was to build a new carrier.", "hypothesis_original": "The top priority for the Navy is to build a new carrier.", "label": 1, "premise": "Ich würde mich nicht wundern, wenn die oberste Priorität für die Navy wäre, einen neuen Träger zu bauen.", "hypothesis": "Die oberste Priorität für die Navy ist es, einen neuen Träger zu bauen." } ``` ## Limitations and bias Machine translation is not as good as human translation. Machine translation can introduce inaccuracies that can be problematic for complex tasks like NLI. In an ideal world, original NLI data would be available for many languages. Given the lack of NLI data, using the latest open-source machine translation seems like a good solution to improve multilingual NLI. You can use the Hugging Face data viewer to inspect the data and verify the translation quality for your language of interest. Note that grammatical errors are less problematic for zero-shot use-cases as grammar is less relevant for these applications. ## Other The machine translation for the full dataset took roughly 100 hours on an A100 GPU, especially due to the size of the [m2m100_1.2B](https://huggingface.co/facebook/m2m100_1.2B) model. ## Ideas for cooperation or questions? For updates on new models and datasets, follow me on [Twitter](https://twitter.com/MoritzLaurer). If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or on [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ### Citation Information If the dataset is useful for you, please cite the following article: ``` @article{laurer_less_2022, title = {Less {Annotating}, {More} {Classifying} – {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT} - {NLI}}, url = {https://osf.io/74b8k}, language = {en-us}, urldate = {2022-07-28}, journal = {Preprint}, author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper}, month = jun, year = {2022}, note = {Publisher: Open Science Framework}, } ```	[ -0.3461539149284363, -0.30856505036354065, 0.1305350512266159, 0.3796112835407257, -0.06482606381177902, -0.19703467190265656, -0.32203903794288635, -0.6050960421562195, 0.36960551142692566, 0.37768763303756714, -0.42406243085861206, -0.5150312781333923, 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RuyuanWan/SBIC_Disagreement	RuyuanWan	2022-12-26T22:07:09Z	308	0	[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended\|social_bias_frames", "language:en", "region:us" ]	[ "text-classification" ]	2022-12-26T18:46:23Z	--- annotations_creators: - crowdsourced language: - en language_creators: - found license: [] multilinguality: - monolingual pretty_name: RuyuanWan/SBIC_Disagreement size_categories: [] source_datasets: - extended\|social_bias_frames tags: [] task_categories: - text-classification task_ids: [] --- This dataset is processed version of Social Bias Inference Corpus(SBIC) dataset including text, annotator's demographics and the annotation disagreement labels. <br> Paper: Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Information <br> Authors: Ruyuan Wan, Jaehyung Kim, Dongyeop Kang <br> Github repo: 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open-llm-leaderboard/details_lilloukas__GPlatty-30B	open-llm-leaderboard	2023-09-23T00:38:28Z	308	0	[ "region:us" ]	null	2023-08-18T00:05:15Z	--- pretty_name: Evaluation run of lilloukas/GPlatty-30B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lilloukas/GPlatty-30B](https://huggingface.co/lilloukas/GPlatty-30B) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 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_lilloukas__GPlatty-30B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T00:38:16.456797](https://huggingface.co/datasets/open-llm-leaderboard/details_lilloukas__GPlatty-30B/blob/main/results_2023-09-23T00-38-16.456797.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\": 0.4629823825503356,\n\ \ \"em_stderr\": 0.005106415513013176,\n \"f1\": 0.5073416526845649,\n\ \ \"f1_stderr\": 0.004906633817362961,\n \"acc\": 0.4742641844979544,\n\ \ \"acc_stderr\": 0.010275992859707792\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.4629823825503356,\n \"em_stderr\": 0.005106415513013176,\n\ \ \"f1\": 0.5073416526845649,\n \"f1_stderr\": 0.004906633817362961\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.13874147081122062,\n \ \ \"acc_stderr\": 0.009521649920798146\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8097868981846882,\n \"acc_stderr\": 0.01103033579861744\n\ \ }\n}\n```" repo_url: https://huggingface.co/lilloukas/GPlatty-30B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|arc:challenge\|25_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T22:25:28.445280.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T00_38_16.456797 path: - '/details_harness\|drop\|3_2023-09-23T00-38-16.456797.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-23T00-38-16.456797.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T00_38_16.456797 path: - '/details_harness\|gsm8k\|5_2023-09-23T00-38-16.456797.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-23T00-38-16.456797.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hellaswag\|10_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hellaswag\|10_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:09:17.218494.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T22:25:28.445280.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T22:25:28.445280.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_09_17.218494 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:09:17.218494.parquet' - split: 2023_07_19T22_25_28.445280 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T22:25:28.445280.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T22:25:28.445280.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T00_38_16.456797 path: - '/details_harness\|winogrande\|5_2023-09-23T00-38-16.456797.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-23T00-38-16.456797.parquet' - config_name: results data_files: - split: 2023_07_19T13_09_17.218494 path: - results_2023-07-19T13:09:17.218494.parquet - split: 2023_07_19T22_25_28.445280 path: - results_2023-07-19T22:25:28.445280.parquet - split: 2023_09_23T00_38_16.456797 path: - results_2023-09-23T00-38-16.456797.parquet - split: latest path: - results_2023-09-23T00-38-16.456797.parquet --- # Dataset Card for Evaluation run of lilloukas/GPlatty-30B ## Dataset Description - Homepage: - Repository: https://huggingface.co/lilloukas/GPlatty-30B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [lilloukas/GPlatty-30B](https://huggingface.co/lilloukas/GPlatty-30B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 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_lilloukas__GPlatty-30B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T00:38:16.456797](https://huggingface.co/datasets/open-llm-leaderboard/details_lilloukas__GPlatty-30B/blob/main/results_2023-09-23T00-38-16.456797.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": 0.4629823825503356, "em_stderr": 0.005106415513013176, "f1": 0.5073416526845649, "f1_stderr": 0.004906633817362961, "acc": 0.4742641844979544, "acc_stderr": 0.010275992859707792 }, "harness\|drop\|3": { "em": 0.4629823825503356, "em_stderr": 0.005106415513013176, "f1": 0.5073416526845649, "f1_stderr": 0.004906633817362961 }, "harness\|gsm8k\|5": { "acc": 0.13874147081122062, "acc_stderr": 0.009521649920798146 }, "harness\|winogrande\|5": { "acc": 0.8097868981846882, "acc_stderr": 0.01103033579861744 } } ``` ### 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]	[ -0.4361157715320587, -0.6612398028373718, 0.2596495449542999, 0.21611323952674866, -0.1702803671360016, 0.1307704597711563, -0.3417533338069916, -0.24298198521137238, 0.447625994682312, 0.4368526339530945, -0.7575531601905823, -0.9121276140213013, -0.6458358764648438, 0.1584114283323288, -0.16182878613471985, 1.1931155920028687, -0.24714745581150055, -0.20933370292186737, 0.017606398090720177, -0.2103741616010666, -0.2474212795495987, -0.4406070411205292, -0.44745591282844543, 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open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V3-16k	open-llm-leaderboard	2023-10-26T22:06:04Z	308	0	[ "region:us" ]	null	2023-10-04T00:04:05Z	--- pretty_name: Evaluation run of totally-not-an-llm/EverythingLM-13b-V3-16k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [totally-not-an-llm/EverythingLM-13b-V3-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V3-16k)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V3-16k\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-26T22:05:52.208686](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V3-16k/blob/main/results_2023-10-26T22-05-52.208686.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\": 0.00975251677852349,\n\ \ \"em_stderr\": 0.0010063982618519684,\n \"f1\": 0.07061661073825479,\n\ \ \"f1_stderr\": 0.001617936937117915,\n \"acc\": 0.3634471116993872,\n\ \ \"acc_stderr\": 0.008309152048639166\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.00975251677852349,\n \"em_stderr\": 0.0010063982618519684,\n\ \ \"f1\": 0.07061661073825479,\n \"f1_stderr\": 0.001617936937117915\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.019711902956785442,\n \ \ \"acc_stderr\": 0.0038289829787357173\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7071823204419889,\n \"acc_stderr\": 0.012789321118542613\n\ \ }\n}\n```" repo_url: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V3-16k leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|arc:challenge\|25_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-10-04T00-03-41.509774.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_26T22_05_52.208686 path: - '/details_harness\|drop\|3_2023-10-26T22-05-52.208686.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-26T22-05-52.208686.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_26T22_05_52.208686 path: - '/details_harness\|gsm8k\|5_2023-10-26T22-05-52.208686.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-26T22-05-52.208686.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hellaswag\|10_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-04T00-03-41.509774.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-management\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-04T00-03-41.509774.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_04T00_03_41.509774 path: - '/details_harness\|truthfulqa:mc\|0_2023-10-04T00-03-41.509774.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-10-04T00-03-41.509774.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_26T22_05_52.208686 path: - '/details_harness\|winogrande\|5_2023-10-26T22-05-52.208686.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-26T22-05-52.208686.parquet' - config_name: results data_files: - split: 2023_10_04T00_03_41.509774 path: - results_2023-10-04T00-03-41.509774.parquet - split: 2023_10_26T22_05_52.208686 path: - results_2023-10-26T22-05-52.208686.parquet - split: latest path: - results_2023-10-26T22-05-52.208686.parquet --- # Dataset Card for Evaluation run of totally-not-an-llm/EverythingLM-13b-V3-16k ## Dataset Description - Homepage: - Repository: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V3-16k - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [totally-not-an-llm/EverythingLM-13b-V3-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V3-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V3-16k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-26T22:05:52.208686](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V3-16k/blob/main/results_2023-10-26T22-05-52.208686.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": 0.00975251677852349, "em_stderr": 0.0010063982618519684, "f1": 0.07061661073825479, "f1_stderr": 0.001617936937117915, "acc": 0.3634471116993872, "acc_stderr": 0.008309152048639166 }, "harness\|drop\|3": { "em": 0.00975251677852349, "em_stderr": 0.0010063982618519684, "f1": 0.07061661073825479, "f1_stderr": 0.001617936937117915 }, "harness\|gsm8k\|5": { "acc": 0.019711902956785442, "acc_stderr": 0.0038289829787357173 }, "harness\|winogrande\|5": { "acc": 0.7071823204419889, "acc_stderr": 0.012789321118542613 } } ``` ### 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]	[ -0.3751201927661896, -0.8130862712860107, 0.3272843658924103, 0.2983640134334564, -0.12651199102401733, 0.1356193721294403, -0.2988869249820709, -0.2823038399219513, 0.4583582878112793, 0.4901447296142578, -0.7560103535652161, -0.9895305633544922, -0.6466646790504456, 0.169117733836174, -0.27622029185295105, 1.1717826128005981, -0.14852778613567352, -0.1686839610338211, 0.02072504535317421, -0.2118581086397171, -0.3152244985103607, -0.4339318573474884, -0.3609825670719147, 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parsinlu_reading_comprehension	null	2023-08-16T17:04:40Z	307	1	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended\|wikipedia\|google", "language:fa", "license:cc-by-nc-sa-4.0", "arxiv:2012.06154", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - fa license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended\|wikipedia\|google task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: null pretty_name: PersiNLU (Reading Comprehension) dataset_info: features: - name: question dtype: string - name: url dtype: string - name: context dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: answer_text dtype: string config_name: parsinlu-repo splits: - name: train num_bytes: 747679 num_examples: 600 - name: test num_bytes: 674711 num_examples: 570 - name: validation num_bytes: 163161 num_examples: 125 download_size: 4105495 dataset_size: 1585527 --- # Dataset Card for PersiNLU (Reading Comprehension) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/persiannlp/parsinlu/) - Repository: [Github](https://github.com/persiannlp/parsinlu/) - Paper: [Arxiv](https://arxiv.org/abs/2012.06154) - Leaderboard: - Point of Contact: [email]([email protected]) ### Dataset Summary A Persian reading comprehenion task (generating an answer, given a question and a context paragraph). The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text dataset is in Persian (`fa`). ## Dataset Structure ### Data Instances Here is an example from the dataset: ``` { 'question': 'پیامبر در چه سالی به پیامبری رسید؟', 'url': 'https://fa.wikipedia.org/wiki/%D9%85%D8%AD%D9%85%D8%AF', 'passage': 'محمد که از روش زندگی مردم مکه ناخشنود بود، گهگاه در غار حرا در یکی از کوه\u200cهای اطراف آن دیار به تفکر و عبادت می\u200cپرداخت. به باور مسلمانان، محمد در همین مکان و در حدود ۴۰ سالگی از طرف خدا به پیامبری برگزیده، و وحی بر او فروفرستاده شد. در نظر آنان، دعوت محمد همانند دعوت دیگر پیامبرانِ کیش یکتاپرستی مبنی بر این بود که خداوند (الله) یکتاست و تسلیم شدن برابر خدا راه رسیدن به اوست.', 'answers': [ {'answer_start': 160, 'answer_text': 'حدود ۴۰ سالگی'} ] } ``` ### Data Fields - `question`: the question, mined using Google auto-complete. - `passage`: the passage that contains the answer. - `url`: the url from which the passage was mined. - `answers`: a list of answers, containing the string and the index of the answer with the fields `answer_start` and `answer_text`. Note that in the test set, some `answer_start` values are missing and replaced with `-1` ### Data Splits The train/test split contains 600/575 samples. ## Dataset Creation ### Curation Rationale The question were collected via Google auto-complete. The answers were annotated by native speakers. For more details, check [the corresponding draft](https://arxiv.org/abs/2012.06154). ### 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 Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC BY-NC-SA 4.0 License ### Citation Information ```bibtex @article{huggingface:dataset, title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian}, authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others}, year={2020} journal = {arXiv e-prints}, eprint = {2012.06154}, } ``` ### Contributions Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.	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ronec	null	2023-01-25T14:43:21Z	307	1	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ro", "license:mit", "arxiv:1909.01247", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - expert-generated - found language: - ro license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: ronec pretty_name: RONEC dataset_info: features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_ids sequence: int32 - name: space_after sequence: bool - name: ner_tags sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-ORG '4': I-ORG '5': B-GPE '6': I-GPE '7': B-LOC '8': I-LOC '9': B-NAT_REL_POL '10': I-NAT_REL_POL '11': B-EVENT '12': I-EVENT '13': B-LANGUAGE '14': I-LANGUAGE '15': B-WORK_OF_ART '16': I-WORK_OF_ART '17': B-DATETIME '18': I-DATETIME '19': B-PERIOD '20': I-PERIOD '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-NUMERIC '26': I-NUMERIC '27': B-ORDINAL '28': I-ORDINAL '29': B-FACILITY '30': I-FACILITY config_name: ronec splits: - name: train num_bytes: 8701577 num_examples: 9000 - name: validation num_bytes: 1266490 num_examples: 1330 - name: test num_bytes: 1902224 num_examples: 2000 download_size: 14675943 dataset_size: 11870291 --- # Dataset Card for RONEC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/dumitrescustefan/ronec - Repository: https://github.com/dumitrescustefan/ronec - Paper: https://arxiv.org/abs/1909.01247 - Leaderboard: https://lirobenchmark.github.io/ - Point of Contact: [Stefan]([email protected]) and [Andrei-Marius]([email protected]) ### Dataset Summary RONEC, at version 2.0, holds 12330 sentences with over 0.5M tokens, annotated with 15 classes, to a total of 80.283 distinctly annotated entities. The corpus has the following classes and distribution in the train/valid/test splits: \| Classes \| Total \| Train \| \| Valid \| \| Test \| \| \|------------- \|:------: \|:------: \|:-------: \|:------: \|:-------: \|:------: \|:-------: \| \| \| # \| # \| % \| # \| % \| # \| % \| \| PERSON \| 26130 \| 19167 \| 73.35 \| 2733 \| 10.46 \| 4230 \| 16.19 \| \| GPE \| 11103 \| 8193 \| 73.79 \| 1182 \| 10.65 \| 1728 \| 15.56 \| \| LOC \| 2467 \| 1824 \| 73.94 \| 270 \| 10.94 \| 373 \| 15.12 \| \| ORG \| 7880 \| 5688 \| 72.18 \| 880 \| 11.17 \| 1312 \| 16.65 \| \| LANGUAGE \| 467 \| 342 \| 73.23 \| 52 \| 11.13 \| 73 \| 15.63 \| \| NAT_REL_POL \| 4970 \| 3673 \| 73.90 \| 516 \| 10.38 \| 781 \| 15.71 \| \| DATETIME \| 9614 \| 6960 \| 72.39 \| 1029 \| 10.7 \| 1625 \| 16.9 \| \| PERIOD \| 1188 \| 862 \| 72.56 \| 129 \| 10.86 \| 197 \| 16.58 \| \| QUANTITY \| 1588 \| 1161 \| 73.11 \| 181 \| 11.4 \| 246 \| 15.49 \| \| MONEY \| 1424 \| 1041 \| 73.10 \| 159 \| 11.17 \| 224 \| 15.73 \| \| NUMERIC \| 7735 \| 5734 \| 74.13 \| 814 \| 10.52 \| 1187 \| 15.35 \| \| ORDINAL \| 1893 \| 1377 \| 72.74 \| 212 \| 11.2 \| 304 \| 16.06 \| \| FACILITY \| 1126 \| 840 \| 74.6 \| 113 \| 10.04 \| 173 \| 15.36 \| \| WORK_OF_ART \| 1596 \| 1157 \| 72.49 \| 176 \| 11.03 \| 263 \| 16.48 \| \| EVENT \| 1102 \| 826 \| 74.95 \| 107 \| 9.71 \| 169 \| 15.34 \| ### Supported Tasks and Leaderboards The corpus is meant to train Named Entity Recognition models for the Romanian language. Please see the leaderboard here : [https://lirobenchmark.github.io/](https://lirobenchmark.github.io/) ### Languages RONEC is in Romanian (`ro`) ## Dataset Structure ### Data Instances The dataset is a list of instances. For example, an instance looks like: ```json { "id": 10454, "tokens": ["Pentru", "a", "vizita", "locația", "care", "va", "fi", "pusă", "la", "dispoziția", "reprezentanților", "consiliilor", "județene", ",", "o", "delegație", "a", "U.N.C.J.R.", ",", "din", "care", "a", "făcut", "parte", "și", "dl", "Constantin", "Ostaficiuc", ",", "președintele", "C.J.T.", ",", "a", "fost", "prezentă", "la", "Bruxelles", ",", "între", "1-3", "martie", "."], "ner_tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PERSON", "O", "O", "O", "O", "O", "O", "B-ORG", "O", "O", "O", "O", "O", "O", "O", "B-PERSON", "I-PERSON", "I-PERSON", "I-PERSON", "I-PERSON", "B-ORG", "O", "O", "O", "O", "O", "B-GPE", "O", "B-PERIOD", "I-PERIOD", "I-PERIOD", "O"], "ner_ids": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 3, 0, 0, 0, 0, 0, 5, 0, 19, 20, 20, 0], "space_after": [true, true, true, true, true, true, true, true, true, true, true, true, false, true, true, true, true, false, true, true, true, true, true, true, true, true, true, false, true, true, false, true, true, true, true, true, false, true, true, true, false, false] } ``` ### Data Fields The fields of each examples are: - ``tokens`` are the words of the sentence. - ``ner_tags`` are the string tags assigned to each token, following the BIO2 format. For example, the span ``"între", "1-3", "martie"`` has three tokens, but is a single class ``PERIOD``, marked as ``"B-PERIOD", "I-PERIOD", "I-PERIOD"``. - ``ner_ids`` are the integer encoding of each tag, to be compatible with the standard and to be quickly used for model training. Note that each ``B``-starting tag is odd, and each ``I``-starting tag is even. - ``space_after`` is used to help if there is a need to detokenize the dataset. A ``true`` value means that there is a space after the token on that respective position. ### Data Splits The dataset is split in train: 9000 sentences, dev: 1330 sentence and test: 2000 sentences. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data The corpus data source represents sentences that are free of copyright, taken from older datasets like the freely available SEETimes and more recent datasources like the Romanian Wikipedia or the Common Crawl. #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations The corpus was annotated with the following classes: 1. PERSON - proper nouns, including common nouns or pronouns if they refer to a person. (e.g. 'sister') 2. GPE - geo political entity, like a city or a country; has to have a governance form 3. LOC - location, like a sea, continent, region, road, address, etc. 4. ORG - organization 5. LANGUAGE - language (e.g. Romanian, French, etc.) 6. NAT_REL_POL - national, religious or political organizations 7. DATETIME - a time and date in any format, including references to time (e.g. 'yesterday') 8. PERIOD - a period that is precisely bounded by two date times 9. QUANTITY - a quantity that is not numerical; it has a unit of measure 10. MONEY - a monetary value, numeric or otherwise 11. NUMERIC - a simple numeric value, represented as digits or words 12. ORDINAL - an ordinal value like 'first', 'third', etc. 13. FACILITY - a named place that is easily recognizable 14. WORK_OF_ART - a work of art like a named TV show, painting, etc. 15. EVENT - a named recognizable or periodic major event #### Annotation process The corpus was annotated by 3 language experts, and was cross-checked for annotation consistency. The annotation took several months to complete, but the result is a high quality dataset. #### Who are the annotators? Stefan Dumitrescu (lead). ### Personal and Sensitive Information All the source data is already freely downloadable and usable online, so there are no privacy concerns. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information MIT License ### Citation Information ```bibtex @article{dumitrescu2019introducing, title={Introducing RONEC--the Romanian Named Entity Corpus}, author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius}, journal={arXiv preprint arXiv:1909.01247}, year={2019} } ``` ### Contributions Thanks to [@iliemihai](https://github.com/iliemihai) for adding v1.0 of the dataset.	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Hellisotherpeople/DebateSum	Hellisotherpeople	2022-12-03T04:14:45Z	307	9	[ "task_categories:question-answering", "task_categories:summarization", "task_categories:text-retrieval", "task_categories:text-generation", "task_ids:abstractive-qa", "task_ids:document-retrieval", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:mit", "conditional-text-generation", "arxiv:2011.07251", "region:us" ]	[ "question-answering", "summarization", "text-retrieval", "text-generation" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering - summarization - text-retrieval - text-generation task_ids: - abstractive-qa - document-retrieval - extractive-qa pretty_name: 'DebateSum: A large-scale argument mining and summarization dataset' language_bcp47: - en-US tags: - conditional-text-generation --- # DebateSum Corresponding code repo for the upcoming paper at ARGMIN 2020: "DebateSum: A large-scale argument mining and summarization dataset" Arxiv pre-print available here: https://arxiv.org/abs/2011.07251 Check out the presentation date and time here: https://argmining2020.i3s.unice.fr/node/9 Full paper as presented by the ACL is here: https://www.aclweb.org/anthology/2020.argmining-1.1/ Video of presentation at COLING 2020: https://underline.io/lecture/6461-debatesum-a-large-scale-argument-mining-and-summarization-dataset The dataset is distributed as csv files. A search engine over DebateSum (as well as some additional evidence not included in DebateSum) is available as [debate.cards](http://debate.cards/). It's very good quality and allows for the evidence to be viewed in the format that debaters use. # Data DebateSum consists of 187328 debate documents, arguements (also can be thought of as abstractive summaries, or queries), word-level extractive summaries, citations, and associated metadata organized by topic-year. This data is ready for analysis by NLP systems. ## Download All data is accesable in a parsed format organized by topic year [here](https://mega.nz/folder/ZdQGmK6b#-0hoBWc5fLYuxQuH25feXg) Addtionally, the trained word-vectors for [debate2vec](https://github.com/Hellisotherpeople/debate2vec) are also found in that folder. ## Regenerating it yourself This is useful as the debaters who produce the evidence release their work every year. Soon enough I will update to include the 2020-2021 topic. Step 1: Download all open evidence files from [Open Evidence](https://openev.debatecoaches.org/) and unzip them into a directory. The links are as follows: * [2019](https://s3.amazonaws.com/openev/2019OpenEv.zip) - Resolved: The United States federal government should substantially reduce Direct Commercial Sales and/or Foreign Military Sales of arms from the United States. * [2018](https://s3.amazonaws.com/openev/2018OpenEv.zip) - Resolved: The United States federal government should substantially reduce its restrictions on legal immigration to the United States. * [2017](https://s3.amazonaws.com/openev/2017OpenEv.zip) - Resolved: The United States federal government should substantially increase its funding and/or regulation of elementary and/or secondary education in the United States. * [2016](https://s3.amazonaws.com/openev/2016OpenEv.zip) - Resolved: The United States federal government should substantially increase its economic and/or diplomatic engagement with the People’s Republic of China. * [2015](https://s3.amazonaws.com/openev/2015OpenEv.zip) - Resolved: The United States federal government should substantially curtail its domestic surveil-lance. * [2014](https://s3.amazonaws.com/openev/2014OpenEv.zip) - Resolved: The United States federal government should substantially increase its non-military exploration and/or development of the Earth’s oceans. * [2013](https://s3.amazonaws.com/openev/2013OpenEv.zip) - Resolved: The United States federal government should substantially increase its economic en-gagement toward Cuba, Mexico or Venezuela. Step 2: Convert all evidence from docx files to html5 files using [pandoc](https://pandoc.org/) with this command: ``` for f in .docx; do pandoc "$f" -s -o "${f%.docx}.html5"; done ``` Step 3: install the dependencies for make_debate_dataset.py.* ``` pip install -r requirements.txt ``` Step 4: Modify the folder and file locations as needed for your system, and run make_debate_dataset.py ``` python3 make_debate_dataset.py ``` # Credits Huge thanks to [Arvind Balaji](https://github.com/arvind-balaji) for making debate.cards and being second author on this paper!	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Olec/cyber-threat-intelligence_v2	Olec	2023-04-15T11:00:18Z	307	4	[ "region:us" ]	null	2023-03-31T15:08:08Z	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: entities list: - name: end_offset dtype: int64 - name: id dtype: int64 - name: label dtype: string - name: start_offset dtype: int64 - name: relations list: - name: from_id dtype: int64 - name: id dtype: int64 - name: to_id dtype: int64 - name: type dtype: string splits: - name: test num_bytes: 29518 num_examples: 72 - name: train num_bytes: 147723 num_examples: 332 - name: validation num_bytes: 36580 num_examples: 76 download_size: 119557 dataset_size: 213821 --- # Dataset Card for "cyber-threat-intelligence_v2" updated version of mrmoor/cyber-threat-intelligence RE and NER Dataset for Cyber Threat Intelegence (CTI) T5 Model trained on NYT and this dataset: Olec/cyber_rebel This dataset only contains sentences with realtions. Full dataset is available at mrmoor/cyber-threat-intelligence.	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open-llm-leaderboard/details_Norquinal__llama-2-7b-claude-chat-rp	open-llm-leaderboard	2023-09-17T21:52:26Z	307	0	[ "region:us" ]	null	2023-08-30T02:08:15Z	--- pretty_name: Evaluation run of Norquinal/llama-2-7b-claude-chat-rp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Norquinal/llama-2-7b-claude-chat-rp](https://huggingface.co/Norquinal/llama-2-7b-claude-chat-rp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Norquinal__llama-2-7b-claude-chat-rp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T21:52:14.967446](https://huggingface.co/datasets/open-llm-leaderboard/details_Norquinal__llama-2-7b-claude-chat-rp/blob/main/results_2023-09-17T21-52-14.967446.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\": 0.001572986577181208,\n\ \ \"em_stderr\": 0.00040584511324177306,\n \"f1\": 0.05937500000000002,\n\ \ \"f1_stderr\": 0.0013421305212247912,\n \"acc\": 0.4101086482368971,\n\ \ \"acc_stderr\": 0.009683376605280784\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.001572986577181208,\n \"em_stderr\": 0.00040584511324177306,\n\ \ \"f1\": 0.05937500000000002,\n \"f1_stderr\": 0.0013421305212247912\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.07278241091736164,\n \ \ \"acc_stderr\": 0.007155604761167465\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7474348855564326,\n \"acc_stderr\": 0.012211148449394105\n\ \ }\n}\n```" repo_url: https://huggingface.co/Norquinal/llama-2-7b-claude-chat-rp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|arc:challenge\|25_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-30T02:07:51.565435.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T21_52_14.967446 path: - '/details_harness\|drop\|3_2023-09-17T21-52-14.967446.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T21-52-14.967446.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T21_52_14.967446 path: - '/details_harness\|gsm8k\|5_2023-09-17T21-52-14.967446.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T21-52-14.967446.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hellaswag\|10_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-30T02:07:51.565435.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-30T02:07:51.565435.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_30T02_07_51.565435 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-30T02:07:51.565435.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-30T02:07:51.565435.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T21_52_14.967446 path: - '/details_harness\|winogrande\|5_2023-09-17T21-52-14.967446.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T21-52-14.967446.parquet' - config_name: results data_files: - split: 2023_08_30T02_07_51.565435 path: - results_2023-08-30T02:07:51.565435.parquet - split: 2023_09_17T21_52_14.967446 path: - results_2023-09-17T21-52-14.967446.parquet - split: latest path: - results_2023-09-17T21-52-14.967446.parquet --- # Dataset Card for Evaluation run of Norquinal/llama-2-7b-claude-chat-rp ## Dataset Description - Homepage: - Repository: https://huggingface.co/Norquinal/llama-2-7b-claude-chat-rp - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Norquinal/llama-2-7b-claude-chat-rp](https://huggingface.co/Norquinal/llama-2-7b-claude-chat-rp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Norquinal__llama-2-7b-claude-chat-rp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T21:52:14.967446](https://huggingface.co/datasets/open-llm-leaderboard/details_Norquinal__llama-2-7b-claude-chat-rp/blob/main/results_2023-09-17T21-52-14.967446.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": 0.001572986577181208, "em_stderr": 0.00040584511324177306, "f1": 0.05937500000000002, "f1_stderr": 0.0013421305212247912, "acc": 0.4101086482368971, "acc_stderr": 0.009683376605280784 }, "harness\|drop\|3": { "em": 0.001572986577181208, "em_stderr": 0.00040584511324177306, "f1": 0.05937500000000002, "f1_stderr": 0.0013421305212247912 }, "harness\|gsm8k\|5": { "acc": 0.07278241091736164, "acc_stderr": 0.007155604761167465 }, "harness\|winogrande\|5": { "acc": 0.7474348855564326, "acc_stderr": 0.012211148449394105 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	[ -0.30363085865974426, -0.7155265808105469, 0.12620383501052856, 0.32096007466316223, -0.14675456285476685, 0.14906349778175354, -0.3588370680809021, -0.26359981298446655, 0.4771145284175873, 0.6364978551864624, -0.7020600438117981, -0.9840830564498901, -0.6672062873840332, 0.12958277761936188, -0.26622989773750305, 1.0885295867919922, -0.1827734112739563, -0.23734773695468903, 0.08752219378948212, -0.2869011461734772, -0.35799214243888855, -0.4548470675945282, -0.5873986482620239, 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kyujinpy/KOR-OpenOrca-Platypus-v3	kyujinpy	2023-11-18T20:22:23Z	307	0	[ "task_categories:conversational", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:10K<n<50K", "language:ko", "license:cc-by-nc-4.0", "arxiv:2306.02707", "arxiv:2301.13688", "region:us" ]	[ "conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation" ]	2023-11-08T18:56:08Z	--- language: - ko license: cc-by-nc-4.0 size_categories: - 10K<n<50K task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation pretty_name: OpenOrca configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: input dtype: string - name: output dtype: string - name: instruction dtype: string splits: - name: train num_examples: 34214 --- # KOR-OpenOrca-Platypus-v3 - KOR-OpenOrca-Platypus 데이터셋에서 수작업으로 번역 오류 200건 이상을 고친 데이터셋. - 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭 ## KOpen-platpyus Repo: [KOpen-platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus) - 고품질 한국어 데이터셋 1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정 2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존 3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴 4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음) 5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정 6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역 7. 고유명사는 최대한 유지함 > Post-processing 작업 내용 - Add post-processing (v2) +) 단답형 Task 삭제. ## OpenOrca-Ko-v2 1. NIV // 약 1500개 2. FLAN // 약 9000개 3. T0 // 약 6000개 4. CoT // 약 2000개 > Dataset 구성 - 수작업으로 고친 내용(v2) 1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...) 2. KoCoT 데이터셋 제거. 3. Yes, True, False 등등 일부 답변 수정 > Post-processing 작업 내용 ## Translation Using DeepL Pro API. Thanks. --- >Below is original dataset card ## Table of Contents - [Dataset Summary](#dataset-summary) - [Dataset Attribution](#dataset-attribution) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Dataset Use](#dataset-use) - [Use Cases](#use-cases) - [Usage Caveats](#usage-caveats) - [Getting Started](#getting-started) <p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p> ![OpenOrca Logo](https://huggingface.co/datasets/Open-Orca/OpenOrca/resolve/main/OpenOrcaLogo.png "OpenOrca Logo") <a name="dataset-announcement"></a> We are thrilled to announce the release of the OpenOrca dataset! This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707). It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers! # Official Models ## OpenOrca-Platypus2-13B Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard! Released in partnership with Platypus. ## LlongOrca 7B & 13B * Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance. * [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance. ## OpenOrcaxOpenChat-Preview2-13B Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper. Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B. Released in partnership with OpenChat. ## OpenOrca-Preview1-13B [OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B) This model was trained in less than a day, for <$200, with <10% of our data. At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper. <a name="dataset-summary"></a> # Dataset Summary The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688). Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions. It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope. The data is primarily used for training and evaluation in the field of natural language processing. <a name="dataset-attribution"></a> # Dataset Attribution We would like to give special recognition to the following contributors for their significant efforts and dedication: Teknium WingLian/Caseus Eric Hartford NanoBit Pankaj Winddude Rohan http://AlignmentLab.ai: Autometa Entropi AtlasUnified NeverendingToast NanoBit WingLian/Caseus Also of course, as always, TheBloke, for being the backbone of the whole community. Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials: http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2). [<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2) <a name="supported-tasks-and-leaderboards"></a> # Supported Tasks and Leaderboards This dataset supports a range of tasks including language modeling, text generation, and text augmentation. It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing. Further information on leaderboards will be updated as they become available. <a name="languages"></a> # Languages The language of the data is primarily English. <a name="dataset-structure"></a> # Dataset Structure <a name="data-instances"></a> ## Data Instances A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5. The response is then entered into the response field. <a name="data-fields"></a> ## Data Fields The fields are: 1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from. 2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint 3) 'question', representing a question entry as provided by the FLAN Collection 4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4. <a name="data-splits"></a> ## Data Splits The data is unsplit. <a name="dataset-creation"></a> # Dataset Creation <a name="curation-rationale"></a> ## Curation Rationale The dataset was created to provide a source of augmented text data for researchers and developers. The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4. This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on. <a name="source-data"></a> ## Source Data The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below: 1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use. We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available. 2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original). These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source. However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively. Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work. <a name="dataset-use"></a> # Dataset Use <a name="use-cases"></a> ## Use Cases The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation. <a name="usage-caveats"></a> ## Usage Caveats Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements. Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper. <a name="getting-started"></a> ## Getting Started This dataset is organized such that it can be naively loaded via Hugging Face datasets library. We recommend using streaming due to the large size of the files. Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face. # Citation ```bibtex @misc{OpenOrca, title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces}, author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca}, } ``` ```bibtex @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint= arXiv 2307.09288 } @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```	[ -0.5971405506134033, 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tamilmixsentiment	null	2023-06-16T13:07:45Z	306	0	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:ta", "license:unknown", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - ta license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Tamilmixsentiment dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Positive '1': Negative '2': Mixed_feelings '3': unknown_state '4': not-Tamil splits: - name: train num_bytes: 790132 num_examples: 11335 - name: validation num_bytes: 89618 num_examples: 1260 - name: test num_bytes: 218764 num_examples: 3149 download_size: 1150792 dataset_size: 1098514 --- # Dataset Card for Tamilmixsentiment ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Tamilmixsentiment Homepage](https://dravidian-codemix.github.io/2020/index.html) - Repository: [Tamilmixsentiment repository](https://dravidian-codemix.github.io/2020/datasets.html) - Paper: [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://www.aclweb.org/anthology/2020.sltu-1.28/) - Leaderboard: [Rank list](https://drive.google.com/file/d/1Mf8-No-63koGRwdF13RrO01NAFBlNmI0/view?usp=sharing) - Point of Contact: [Bharathi Raja Chakravarthi](mailto:[email protected]) ### Dataset Summary The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated with sentiment polarity at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. ### Supported Tasks and Leaderboards To identify sentiment polarity of the code-mixed dataset of comments/posts in Tamil-English collected from social media. ### Languages Tamil-English code-switched. The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between. ## Dataset Structure ### Data Instances An example from the Tamilmixsentiment train set looks as follows: ``` text label Trailer late ah parthavanga like podunga Positive ``` ### Data Fields - `text`: Tamil-English code-mixed comment. - `label`: list of the possible sentiments "Positive", "Negative", "Mixed_feelings", "unknown_state", "not-Tamil" ### Data Splits The entire dataset of 15,744 sentences was randomly shuffled and split into three parts as follows: \| \| train \| validation \| test \| \|------------------------------\|------:\|-----------:\|-----:\| \| Tamilmixsentiment \| 11335 \| 1260 \| 3149 \| ## Dataset Creation ### Curation Rationale Sentiment analysis has become important in social media research (Yang and Eisenstein, 2017). Until recently these applications were created for high-resourced languages which analysed monolingual utterances. But social media in multilingual communities contains more code-mixed text. Code-mixing is common among speakers in a bilingual speech community. As English is seen as the language of prestige and education, the influence of lexicon, connectives and phrases from English language is common in spoken Tamil. Tamil has little annotated data for code-mixed scenarios. An annotated corpus developed for monolingual data cannot deal with code-mixed usage and therefore it fails to yield good results due to mixture of languages at different levels of linguistic analysis. Therefore this dataset of code-mixed Tamil-English sentiment annotated corpus is created. ### Source Data #### Initial Data Collection and Normalization The data was scraped from Youtube. In total 184,573 sentences for Tamil from YouTube comments from the trailers of a movies released in 2019. Many of the them contained sentences that were either entirely written in English or code-mixed Tamil-English or fully written in Tamil. So we filtered out a non-code-mixed corpus based on language identification at comment level using the langdetect library. The comment is written fully in Tamil or English, we discarded that comment since monolingual resources are available for these languages. We also identified if the sentences were written in other languages such as Hindi, Malayalam, Urdu, Telugu, and Kannada. We preprocessed the comments by removing the emoticons and applying a sentence length filter. We want to create a code-mixed corpus of reasonable size with sentences that have fairly defined sentiments which will be useful for future research. Thus our filter removed sentences with less than five words and more than 15 words after cleaning the data. In the end we got 15,744 Tanglish sentences. #### Who are the source language producers? Youtube users ### Annotations #### Annotation process Three steps complete the annotation setup. First, each sentence was annotated by two people. In the second step, the data were collected if both of them agreed. In the case of conflict, a third person annotated the sentence. In the third step, if all the three of them did not agree, then two more annotators annotated the sentences. #### Who are the annotators? Eleven volunteers were involved in the process. All of them were native speakers of Tamil with diversity in gender, educational level and medium of instruction in their school education. ### 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 ``` @inproceedings{chakravarthi-etal-2020-corpus, title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text", author = "Chakravarthi, Bharathi Raja and Muralidaran, Vigneshwaran and Priyadharshini, Ruba and McCrae, John Philip", booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources association", url = "https://www.aclweb.org/anthology/2020.sltu-1.28", pages = "202--210", abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.", language = "English", ISBN = "979-10-95546-35-1", } ``` ### Contributions Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset.	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DDSC/twitter-sent	DDSC	2022-07-01T15:44:26Z	306	3	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:da", "license:cc-by-4.0", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - found language: - da license: - cc-by-4.0 multilinguality: - monolingual pretty_name: TwitterSent size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for DKHate ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Direct Download: https://danlp.alexandra.dk/304bd159d5de/datasets/twitter.sentiment.zip ### Dataset Summary This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis by the [Alexandra Institute](https://github.com/alexandrainst) - all credits go to them. ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a tweet and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - `text` (`str`): The tweet content. - `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A `train` and `test` split is available, being identical to the original splits. There are 1,007 tweets in the training split and 431 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the [Alexandra Institute](https://github.com/alexandrainst). The tweets have been anonymised by [@saattrupdan](https://github.com/saattrupdan). ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Citation Information ``` @misc{twittersent, title={TwitterSent}, author={Alexandra Institute}, year={2020}, note={\url{https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent}} } ``` ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.	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carolina-c4ai/corpus-carolina	carolina-c4ai	2023-03-23T19:46:16Z	306	12	[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:masked-language-modeling", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1B<n<10B", "source_datasets:original", "language:pt", "license:cc-by-nc-sa-4.0", "region:us" ]	[ "fill-mask", "text-generation" ]	2022-03-28T13:30:33Z	--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - pt license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 1B<n<10B source_datasets: - original task_categories: - fill-mask - text-generation task_ids: - masked-language-modeling - language-modeling pretty_name: Carolina language_bcp47: - pt-BR --- # Dataset Card for Corpus Carolina ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [sites.usp.br/corpuscarolina](https://sites.usp.br/corpuscarolina/) - Current Version: 1.2 (Ada) - Point of Contact: [LaViHD](mailto:[email protected]) ### Dataset Summary Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a robust volume of texts of varied typology in contemporary Brazilian Portuguese (1970-2021). This corpus contains documents and texts extracted from the web and includes information (metadata) about its provenance and tipology. The documents are clustered into taxonomies and the corpus can be loaded in complete or taxonomy modes. To load a single taxonomy, it is possible to pass a code as a parameter to the loading script (see the example bellow). Codes are 3-letters string and possible values are: - `dat` : datasets and other corpora; - `jud` : judicial branch; - `leg` : legislative branch; - `pub` : public domain works; - `soc` : social media; - `uni` : university domains; - `wik` : wikis. Dataset Vesioning: The Carolina Corpus is under continuous development resulting in multiple vesions. The current version is v1.2, but v1.1 is also available. You can access diferent vesions of the corpus using the `revision` parameter on `load_dataset`. Usage Example: ```python from datasets import load_dataset # to load all taxonomies corpus_carolina = load_dataset("carolina-c4ai/corpus-carolina") # to load social media documents social_media = load_dataset("carolina-c4ai/corpus-carolina", taxonomy="soc") # to load previous version corpus_carolina = load_dataset("carolina-c4ai/corpus-carolina", revision="v1.1") ``` ### Supported Tasks Carolina corpus was compiled for academic purposes, namely linguistic and computational analysis. ### Languages Contemporary Brazilian Portuguese (1970-2021). ## Dataset Structure Files are stored inside `corpus` folder with a subfolder for each taxonomy. Every file folows a XML structure (TEI P5) and contains multiple extracted documents. For each document, the text and metadata are exposed as `text` and `meta` features, respectively. ### Data Instances Every instance have the following structure. ``` { "meta": datasets.Value("string"), "text": datasets.Value("string") } ``` \| Code \| Taxonomy \| Instances \| Size \| \|:----:\|:---------------------------\|----------:\|-------:\| \| \| Total \| 2107045 \| 11 GB \| \| dat \| Datasets and other Corpora \| 1102049 \| 4.4 GB \| \| wik \| Wikis \| 960139 \| 5.2 GB \| \| jud \| Judicial Branch \| 40464 \| 1.5 GB \| \| leg \| Legislative Branch \| 13 \| 25 MB \| \| soc \| Social Media \| 3413 \| 17 MB \| \| uni \| University Domains \| 941 \| 10 MB \| \| pub \| Public Domain Works \| 26 \| 4.5 MB \| \|\| ### Data Fields - `meta`: a XML string with a TEI conformant `teiHeader` tag. It is exposed as text and needs to be parsed in order to access the actual metada; - `text`: a string containing the extracted document. ### Data Splits As a general corpus, Carolina does not have splits. In order to load the dataset, it is used `corpus` as its single split. ## Additional Information ### Dataset Curators The Corpus Carolina is developed by a multidisciplinary team of linguists and computer scientists, members of the Virtual Laboratory of Digital Humanities - LaViHD and the Artificial Intelligence Center of the University of São Paulo - C4AI. ### Licensing Information The Open Corpus for Linguistics and Artificial Intelligence (Carolina) was compiled for academic purposes, namely linguistic and computational analysis. It is composed of texts assembled in various digital repositories, whose licenses are multiple and therefore should be observed when making use of the corpus. The Carolina headers are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International." ### Citation Information ``` @misc{corpusCarolinaV1.1, title={ Carolina: The Open Corpus for Linguistics and Artificial Intelligence }, author={ Finger, Marcelo and Paixão de Sousa, Maria Clara and Namiuti, Cristiane and Martins do Monte, Vanessa and Costa, Aline Silva and Serras, Felipe Ribas and Sturzeneker, Mariana Lourenço and Guets, Raquel de Paula and Mesquita, Renata Morais and Mello, Guilherme Lamartine de and Crespo, Maria Clara Ramos Morales and Rocha, Maria Lina de Souza Jeannine and Brasil, Patrícia and Silva, Mariana Marques da and Palma, Mayara Feliciano }, howpublished={\url{ https://sites.usp.br/corpuscarolina/corpus}}, year={2022}, note={Version 1.1 (Ada)}, } ```	[ -0.5772516131401062, -0.439620703458786, 0.40210118889808655, 0.31080299615859985, -0.2789466679096222, 0.36610063910484314, -0.5926422476768494, -0.5524302124977112, 0.5906434059143066, 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sbu_captions	null	2023-06-02T20:56:01Z	306	11	[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:unknown", "region:us" ]	[ "image-to-text" ]	2022-04-12T10:41:52Z	--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - image-to-text task_ids: - image-captioning paperswithcode_id: sbu-captions-dataset pretty_name: SBU Captioned Photo Dataset dataset_info: features: - name: image_url dtype: string - name: user_id dtype: string - name: caption dtype: string splits: - name: train num_bytes: 143795586 num_examples: 1000000 download_size: 49787719 dataset_size: 143795586 --- # Dataset Card for SBU Captioned Photo Dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.cs.rice.edu/~vo9/sbucaptions/ - Repository: - Paper: [Im2Text: Describing Images Using 1 Million Captioned Photographs](https://papers.nips.cc/paper/2011/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html) - Leaderboard: - Point of Contact: [Vicente Ordóñez Román](mailto:[email protected]) ### Dataset Summary SBU Captioned Photo Dataset is a collection of associated captions and images from Flickr. ### Dataset Preprocessing This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code: ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent USER_AGENT = get_datasets_user_agent() def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": USER_AGENT}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"])) return batch num_threads = 20 dset = load_dataset("sbu_captions") dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads}) ``` ### Supported Tasks and Leaderboards - `image-to-text`: This dataset can be used to train a model for Image Captioning where the goal is to predict a caption given the image. ### Languages All captions are in English. ## Dataset Structure ### Data Instances Each instance in SBU Captioned Photo Dataset represents a single image with a caption and a user_id: ``` { 'img_url': 'http://static.flickr.com/2723/4385058960_b0f291553e.jpg', 'user_id': '47889917@N08', 'caption': 'A wooden chair in the living room' } ``` ### Data Fields - `image_url`: Static URL for downloading the image associated with the post. - `caption`: Textual description of the image. - `user_id`: Author of caption. ### Data Splits All the data is contained in training split. The training set has 1M instances. ## Dataset Creation ### Curation Rationale From the paper: > One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. ### Source Data The source images come from Flickr. #### Initial Data Collection and Normalization One key contribution of our paper is a novel web-scale database of photographs with associated descriptive text. To enable effective captioning of novel images, this database must be good in two ways: 1) It must be large so that image based matches to a query are reasonably similar, 2) The captions associated with the data base photographs must be visually relevant so that transferring captions between pictures is useful. To achieve the first requirement we query Flickr using a huge number of pairs of query terms (objects, attributes, actions, stuff, and scenes). This produces a very large, but noisy initial set of photographs with associated text. #### Who are the source language producers? The Flickr users. ### Annotations #### Annotation process Text descriptions associated with the images are inherited as annotations/captions. #### Who are the annotators? The Flickr users. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators Vicente Ordonez, Girish Kulkarni and Tamara L. Berg. ### Licensing Information Not specified. ### Citation Information ```bibtex @inproceedings{NIPS2011_5dd9db5e, author = {Ordonez, Vicente and Kulkarni, Girish and Berg, Tamara}, booktitle = {Advances in Neural Information Processing Systems}, editor = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger}, pages = {}, publisher = {Curran Associates, Inc.}, title = {Im2Text: Describing Images Using 1 Million Captioned Photographs}, url = {https://proceedings.neurips.cc/paper/2011/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf}, volume = {24}, year = {2011} } ``` ### Contributions Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset	[ -0.6098219156265259, -0.42608219385147095, -0.04315829649567604, 0.05996962636709213, -0.6128121614456177, -0.1305500566959381, -0.08381091058254242, -0.4462847113609314, 0.06422878801822662, 0.5302257537841797, -0.6977277398109436, -0.3145376741886139, -0.5349024534225464, 0.32217371463775635, -0.35363608598709106, 1.0321483612060547, 0.13734209537506104, 0.026769695803523064, -0.36068451404571533, -0.26216909289360046, -0.15608501434326172, 0.03149395436048508, -0.7417770624160767, 0.0077695660293102264, 0.27514463663101196, 0.29148349165916443, 0.7049918174743652, 0.7740699648857117, 0.8443612456321716, 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Deysi/spam-detection-dataset	Deysi	2023-04-15T17:42:24Z	306	5	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]	[ "text-classification" ]	2023-04-15T17:39:24Z	--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 3161821 num_examples: 8175 - name: test num_bytes: 1094757 num_examples: 2725 download_size: 2578551 dataset_size: 4256578 license: apache-2.0 task_categories: - text-classification language: - en pretty_name: spam size_categories: - 10K<n<100K --- # Dataset Card for "spam-detection-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6982182264328003, -0.3924075961112976, 0.06377249956130981, 0.3701009452342987, -0.14463648200035095, -0.04414552450180054, 0.2529272735118866, -0.03008458763360977, 0.6646041870117188, 0.67235267162323, 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open-llm-leaderboard/details_garage-bAInd__Platypus2-13B	open-llm-leaderboard	2023-09-18T01:36:25Z	306	0	[ "region:us" ]	null	2023-08-18T00:08:08Z	--- pretty_name: Evaluation run of garage-bAInd/Platypus2-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [garage-bAInd/Platypus2-13B](https://huggingface.co/garage-bAInd/Platypus2-13B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_garage-bAInd__Platypus2-13B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-18T01:36:13.109840](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Platypus2-13B/blob/main/results_2023-09-18T01-36-13.109840.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\": 0.0065016778523489934,\n\ \ \"em_stderr\": 0.0008230684297224003,\n \"f1\": 0.06950713087248322,\n\ \ \"f1_stderr\": 0.001573785110075933,\n \"acc\": 0.4196265138319013,\n\ \ \"acc_stderr\": 0.009450791969417059\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0065016778523489934,\n \"em_stderr\": 0.0008230684297224003,\n\ \ \"f1\": 0.06950713087248322,\n \"f1_stderr\": 0.001573785110075933\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.07050796057619409,\n \ \ \"acc_stderr\": 0.007051543813983609\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\ \ }\n}\n```" repo_url: https://huggingface.co/garage-bAInd/Platypus2-13B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|arc:challenge\|25_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-09T15:47:08.071954.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_18T01_36_13.109840 path: - '/details_harness\|drop\|3_2023-09-18T01-36-13.109840.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-18T01-36-13.109840.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_18T01_36_13.109840 path: - '/details_harness\|gsm8k\|5_2023-09-18T01-36-13.109840.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-18T01-36-13.109840.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hellaswag\|10_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T15:47:08.071954.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T15:47:08.071954.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T15_47_08.071954 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T15:47:08.071954.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T15:47:08.071954.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_18T01_36_13.109840 path: - '/details_harness\|winogrande\|5_2023-09-18T01-36-13.109840.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-18T01-36-13.109840.parquet' - config_name: results data_files: - split: 2023_08_09T15_47_08.071954 path: - results_2023-08-09T15:47:08.071954.parquet - split: 2023_09_18T01_36_13.109840 path: - results_2023-09-18T01-36-13.109840.parquet - split: latest path: - results_2023-09-18T01-36-13.109840.parquet --- # Dataset Card for Evaluation run of garage-bAInd/Platypus2-13B ## Dataset Description - Homepage: - Repository: https://huggingface.co/garage-bAInd/Platypus2-13B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [garage-bAInd/Platypus2-13B](https://huggingface.co/garage-bAInd/Platypus2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_garage-bAInd__Platypus2-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-18T01:36:13.109840](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Platypus2-13B/blob/main/results_2023-09-18T01-36-13.109840.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": 0.0065016778523489934, "em_stderr": 0.0008230684297224003, "f1": 0.06950713087248322, "f1_stderr": 0.001573785110075933, "acc": 0.4196265138319013, "acc_stderr": 0.009450791969417059 }, "harness\|drop\|3": { "em": 0.0065016778523489934, "em_stderr": 0.0008230684297224003, "f1": 0.06950713087248322, "f1_stderr": 0.001573785110075933 }, "harness\|gsm8k\|5": { "acc": 0.07050796057619409, "acc_stderr": 0.007051543813983609 }, "harness\|winogrande\|5": { "acc": 0.7687450670876085, "acc_stderr": 0.01185004012485051 } } ``` ### 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]	[ -0.40043455362319946, -0.6160402297973633, 0.2267848551273346, 0.26809588074684143, -0.17487987875938416, 0.21229369938373566, -0.420354425907135, -0.09366900473833084, 0.3255528211593628, 0.47958648204803467, -0.7484704256057739, -0.9237743020057678, -0.6263710260391235, 0.13448907434940338, -0.09310106933116913, 1.0494415760040283, -0.2848106920719147, -0.19546711444854736, -0.014743219129741192, -0.24257856607437134, -0.4117843210697174, -0.43685218691825867, -0.44923317432403564, 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covid_tweets_japanese	null	2023-01-25T14:28:47Z	305	1	[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ja", "license:cc-by-nd-4.0", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - crowdsourced language_creators: - found language: - ja license: - cc-by-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking pretty_name: COVID-19 日本語Twitterデータセット (COVID-19 Japanese Twitter Dataset) dataset_info: features: - name: tweet_id dtype: string - name: assessment_option_id dtype: class_label: names: '0': '63' '1': '64' '2': '65' '3': '66' '4': '67' '5': '68' splits: - name: train num_bytes: 1662833 num_examples: 53639 download_size: 406005 dataset_size: 1662833 --- # Dataset Card for COVID-19 日本語Twitterデータセット (COVID-19 Japanese Twitter Dataset) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [COVID-19 日本語Twitterデータセット homepage](http://www.db.info.gifu-u.ac.jp/data/Data_5f02db873363f976fce930d1) - Repository: [N/A] - Paper: [N/A] - Leaderboard: [N/A] - Point of Contact: Check the homepage. ### Dataset Summary 53,640 Japanese tweets with annotation if a tweet is related to COVID-19 or not. The annotation is by majority decision by 5 - 10 crowd workers. Target tweets include "COVID" or "コロナ". The period of the tweets is from around January 2020 to around June 2020. The original tweets are not contained. Please use Twitter API to get them, for example. ### Supported Tasks and Leaderboards Text-classification, Whether the tweet is related to COVID-19, and whether it is fact or opinion. ### Languages The text can be gotten using the IDs in this dataset is Japanese, posted on Twitter. ## Dataset Structure ### Data Instances CSV file with the 1st column is Twitter ID and the 2nd column is assessment option ID. ### Data Fields - `tweet_id`: Twitter ID. - `assessment_option_id`: The selection result. It has the following meanings: - 63: a general fact: generally published information, such as news. - 64: a personal fact: personal news. For example, a person heard that the next-door neighbor, XX, has infected COVID-19, which has not been in a news. - 65: an opinion/feeling - 66: difficult to determine if they are related to COVID-19 (it is definitely the tweet is not "67: unrelated", but 63, 64, 65 cannot be determined) - 67: unrelated - 68: it is a fact, but difficult to determine whether general facts, personal facts, or impressions (it may be irrelevant to COVID-19 since it is indistinguishable between 63 - 65 and 67). ### Data Splits No articles have been published for this dataset, and it appears that the author of the dataset is willing to publish an article (it is not certain that the splitting information will be included). Therefore, at this time, information on data splits is not provided. ## Dataset Creation ### Curation Rationale [More Information Needed] because the paper is not yet published. ### Source Data #### Initial Data Collection and Normalization 53,640 Japanese tweets with annotation if a tweet is related to COVID-19 or not. Target tweets include "COVID" or "コロナ". The period of the tweets is from around January 2020 to around June 2020. #### Who are the source language producers? The language producers are users of Twitter. ### Annotations #### Annotation process The annotation is by majority decision by 5 - 10 crowd workers. #### Who are the annotators? Crowd workers. ### Personal and Sensitive Information The author does not contain original tweets. ## 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 The dataset is hosted by Suzuki Laboratory, Gifu University, Japan. ### Licensing Information CC-BY-ND 4.0 ### Citation Information A related paper has not yet published. The author shows how to cite as「鈴木優: COVID-19 日本語 Twitter データセット（ http://www.db.info.gifu-u.ac.jp/data/Data_5f02db873363f976fce930d1 ）」. ### Contributions Thanks to [@forest1988](https://github.com/forest1988) for adding this dataset.	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linnaeus	null	2023-06-15T14:40:39Z	305	1	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: linnaeus pretty_name: LINNAEUS dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B '2': I config_name: linnaeus splits: - name: train num_bytes: 4772417 num_examples: 11936 - name: validation num_bytes: 1592823 num_examples: 4079 - name: test num_bytes: 2802877 num_examples: 7143 download_size: 18204624 dataset_size: 9168117 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [linnaeus](http://linnaeus.sourceforge.net/) - Repository: https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/linnaeus-IOB - Paper: [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85) - Leaderboard: - Point of Contact: ### Dataset Summary The LINNAEUS corpus consists of 100 full-text documents from the PMCOA document set which were randomly selected. All mentions of species terms were manually annotated and normalized to the NCBI taxonomy IDs of the intended species. The original LINNAEUS corpus is available in a TAB-separated standoff format. The resource does not define training, development or test subsets. We converted the corpus into BioNLP shared task standoff format using a custom script, split it into 50-, 17-, and 33- document training, development and test sets, and then converted these into the CoNLL format using standoff2conll. As a full-text corpus, LINNAEUS contains comparatively frequent non-ASCII characters, which were mapped to ASCII using the standoff2conll -a option. The conversion was highly accurate, but due to sentence-splitting errors within entity mentions, the number of annotations in the converted data was larger by four (100.09%) than that in the source data. 99.77% of names in the original annotation matched names in the converted data. ### Supported Tasks and Leaderboards This dataset is used for species Named Entity Recognition. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances An example from the dataset is: ``` {'id': '2', 'tokens': ['Scp160p', 'is', 'a', '160', 'kDa', 'protein', 'in', 'the', 'yeast', 'Saccharomyces', 'cerevisiae', 'that', 'contains', '14', 'repeats', 'of', 'the', 'hnRNP', 'K', '-', 'homology', '(', 'KH', ')', 'domain', ',', 'and', 'demonstrates', 'significant', 'sequence', 'homology', 'to', 'a', 'family', 'of', 'proteins', 'collectively', 'known', 'as', 'vigilins', '.'], 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} ``` ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no species mentioned, `1` signals the first token of a species and `2` the subsequent tokens of the species. ### Data Splits \| name \|train\|validation\|test\| \|----------\|----:\|---------:\|---:\| \| linnaeus \|11936\| 4079\|7143\| ## 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 This version of the dataset is licensed under [Creative Commons Attribution 4.0 International](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/blob/master/LICENSE.md). ### Citation Information ```bibtex @article{crichton2017neural, title={A neural network multi-task learning approach to biomedical named entity recognition}, author={Crichton, Gamal and Pyysalo, Sampo and Chiu, Billy and Korhonen, Anna}, journal={BMC Bioinformatics}, volume={18}, number={1}, pages={368}, year={2017}, publisher={BioMed Central} doi = {10.1186/s12859-017-1776-8}, issn = {1471-2105}, url = {https://doi.org/10.1186/s12859-017-1776-8}, } @article{Gerner2010, author = {Gerner, Martin and Nenadic, Goran and Bergman, Casey M}, doi = {10.1186/1471-2105-11-85}, issn = {1471-2105}, journal = {BMC Bioinformatics}, number = {1}, pages = {85}, title = {{LINNAEUS: A species name identification system for biomedical literature}}, url = {https://doi.org/10.1186/1471-2105-11-85}, volume = {11}, year = {2010} } ``` ### Contributions Thanks to [@edugp](https://github.com/edugp) for adding this dataset.	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norne	null	2023-01-25T14:41:42Z	305	1	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:no", "license:other", "arxiv:1911.12146", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - 'no' license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: 'NorNE: Norwegian Named Entities' dataset_info: - config_name: bokmaal features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-GPE_LOC '6': I-GPE_LOC '7': B-PROD '8': I-PROD '9': B-LOC '10': I-LOC '11': B-GPE_ORG '12': I-GPE_ORG '13': B-DRV '14': I-DRV '15': B-EVT '16': I-EVT '17': B-MISC '18': I-MISC splits: - name: train num_bytes: 10032169 num_examples: 15696 - name: validation num_bytes: 1501730 num_examples: 2410 - name: test num_bytes: 1234272 num_examples: 1939 download_size: 20909241 dataset_size: 12768171 - config_name: nynorsk features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-GPE_LOC '6': I-GPE_LOC '7': B-PROD '8': I-PROD '9': B-LOC '10': I-LOC '11': B-GPE_ORG '12': I-GPE_ORG '13': B-DRV '14': I-DRV '15': B-EVT '16': I-EVT '17': B-MISC '18': I-MISC splits: - name: train num_bytes: 10072260 num_examples: 14174 - name: validation num_bytes: 1278029 num_examples: 1890 - name: test num_bytes: 1023358 num_examples: 1511 download_size: 20209253 dataset_size: 12373647 - config_name: combined features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-GPE_LOC '6': I-GPE_LOC '7': B-PROD '8': I-PROD '9': B-LOC '10': I-LOC '11': B-GPE_ORG '12': I-GPE_ORG '13': B-DRV '14': I-DRV '15': B-EVT '16': I-EVT '17': B-MISC '18': I-MISC splits: - name: train num_bytes: 20104393 num_examples: 29870 - name: validation num_bytes: 2779723 num_examples: 4300 - name: test num_bytes: 2257594 num_examples: 3450 download_size: 41118494 dataset_size: 25141710 - config_name: bokmaal-7 features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-PROD '6': I-PROD '7': B-LOC '8': I-LOC '9': B-DRV '10': I-DRV '11': B-EVT '12': I-EVT '13': B-MISC '14': I-MISC splits: - name: train num_bytes: 10032169 num_examples: 15696 - name: validation num_bytes: 1501730 num_examples: 2410 - name: test num_bytes: 1234272 num_examples: 1939 download_size: 20909241 dataset_size: 12768171 - config_name: nynorsk-7 features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-PROD '6': I-PROD '7': B-LOC '8': I-LOC '9': B-DRV '10': I-DRV '11': B-EVT '12': I-EVT '13': B-MISC '14': I-MISC splits: - name: train num_bytes: 10072260 num_examples: 14174 - name: validation num_bytes: 1278029 num_examples: 1890 - name: test num_bytes: 1023358 num_examples: 1511 download_size: 20209253 dataset_size: 12373647 - config_name: combined-7 features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-PROD '6': I-PROD '7': B-LOC '8': I-LOC '9': B-DRV '10': I-DRV '11': B-EVT '12': I-EVT '13': B-MISC '14': I-MISC splits: - name: train num_bytes: 20104393 num_examples: 29870 - name: validation num_bytes: 2779723 num_examples: 4300 - name: test num_bytes: 2257594 num_examples: 3450 download_size: 41118494 dataset_size: 25141710 - config_name: bokmaal-8 features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-PROD '6': I-PROD '7': B-LOC '8': I-LOC '9': B-GPE '10': I-GPE '11': B-DRV '12': I-DRV '13': B-EVT '14': I-EVT '15': B-MISC '16': I-MISC splits: - name: train num_bytes: 10032169 num_examples: 15696 - name: validation num_bytes: 1501730 num_examples: 2410 - name: test num_bytes: 1234272 num_examples: 1939 download_size: 20909241 dataset_size: 12768171 - config_name: nynorsk-8 features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-PROD '6': I-PROD '7': B-LOC '8': I-LOC '9': B-GPE '10': I-GPE '11': B-DRV '12': I-DRV '13': B-EVT '14': I-EVT '15': B-MISC '16': I-MISC splits: - name: train num_bytes: 10072260 num_examples: 14174 - name: validation num_bytes: 1278029 num_examples: 1890 - name: test num_bytes: 1023358 num_examples: 1511 download_size: 20209253 dataset_size: 12373647 - config_name: combined-8 features: - name: idx dtype: string - name: lang dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NOUN '1': PUNCT '2': ADP '3': NUM '4': SYM '5': SCONJ '6': ADJ '7': PART '8': DET '9': CCONJ '10': PROPN '11': PRON '12': X '13': ADV '14': INTJ '15': VERB '16': AUX - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-PROD '6': I-PROD '7': B-LOC '8': I-LOC '9': B-GPE '10': I-GPE '11': B-DRV '12': I-DRV '13': B-EVT '14': I-EVT '15': B-MISC '16': I-MISC splits: - name: train num_bytes: 20104393 num_examples: 29870 - name: validation num_bytes: 2779723 num_examples: 4300 - name: test num_bytes: 2257594 num_examples: 3450 download_size: 41118494 dataset_size: 25141710 --- # Dataset Card for NorNE: Norwegian Named Entities ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [NorNE](https://github.com/ltgoslo/norne/) - Repository: [Github](https://github.com/ltgoslo/norne/) - Paper: https://arxiv.org/abs/1911.12146 - Leaderboard: - Point of Contact: ### Dataset Summary NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons,organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. There are 3 main configs in this dataset each with 3 versions of the NER tag set. When accessing the `bokmaal`, `nynorsk`, or `combined` configs the NER tag set will be comprised of 9 tags: `GPE_ORG`, `GPE_LOC`, `ORG`, `LOC`, `PER`, `PROD`, `EVT`, `DRV`, and `MISC`. The two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. To access these reduced versions of the dataset, you can use the configs `bokmaal-7`, `nynorsk-7`, `combined-7` for the NER tag set with 7 tags ( `ORG`, `LOC`, `PER`, `PROD`, `EVT`, `DRV`, `MISC`), and `bokmaal-8`, `nynorsk-8`, `combined-8` for the NER tag set with 8 tags (`LOC_` and `ORG_`: `ORG`, `LOC`, `GPE`, `PER`, `PROD`, `EVT`, `DRV`, `MISC`). By default, the full set (9 tags) will be used. See Annotations for further details. ### Supported Tasks and Leaderboards NorNE ads named entity annotations on top of the Norwegian Dependency Treebank. ### Languages Both Norwegian Bokmål (`bokmaal`) and Nynorsk (`nynorsk`) are supported as different configs in this dataset. An extra config for the combined languages is also included (`combined`). See the Annotation section for details on accessing reduced tag sets for the NER feature. ## Dataset Structure Each entry contains text sentences, their language, identifiers, tokens, lemmas, and corresponding NER and POS tag lists. ### Data Instances An example of the `train` split of the `bokmaal` config. ```python {'idx': '000001', 'lang': 'bokmaal', 'lemmas': ['lam', 'og', 'piggvar', 'på', 'bryllupsmeny'], 'ner_tags': [0, 0, 0, 0, 0], 'pos_tags': [0, 9, 0, 5, 0], 'text': 'Lam og piggvar på bryllupsmenyen', 'tokens': ['Lam', 'og', 'piggvar', 'på', 'bryllupsmenyen']} ``` ### Data Fields Each entry is annotated with the next fields: - `idx` (`int`), text (sentence) identifier from the NorNE dataset - `lang` (`str`), language variety, either `bokmaal`, `nynorsk` or `combined` - `text` (`str`), plain text - `tokens` (`List[str]`), list of tokens extracted from `text` - `lemmas` (`List[str]`), list of lemmas extracted from `tokens` - `ner_tags` (`List[int]`), list of numeric NER tags for each token in `tokens` - `pos_tags` (`List[int]`), list of numeric PoS tags for each token in `tokens` An example DataFrame obtained from the dataset: <table class="dataframe" border="1"> <thead> <tr style="text-align: right;"> <th></th> <th>idx</th> <th>lang</th> <th>text</th> <th>tokens</th> <th>lemmas</th> <th>ner_tags</th> <th>pos_tags</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>000001</td> <td>bokmaal</td> <td>Lam og piggvar på bryllupsmenyen</td> <td>[Lam, og, piggvar, på, bryllupsmenyen]</td> <td>[lam, og, piggvar, på, bryllupsmeny]</td> <td>[0, 0, 0, 0, 0]</td> <td>[0, 9, 0, 5, 0]</td> </tr> <tr> <th>1</th> <td>000002</td> <td>bokmaal</td> <td>Kamskjell, piggvar og lammefilet sto på menyen...</td> <td>[Kamskjell, ,, piggvar, og, lammefilet, sto, p...</td> <td>[kamskjell, $,, piggvar, og, lammefilet, stå, ...</td> <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td> <td>[0, 1, 0, 9, 0, 15, 2, 0, 2, 8, 6, 0, 1]</td> </tr> <tr> <th>2</th> <td>000003</td> <td>bokmaal</td> <td>Og til dessert: Parfait à la Mette-Marit.</td> <td>[Og, til, dessert, :, Parfait, à, la, Mette-Ma...</td> <td>[og, til, dessert, $:, Parfait, à, la, Mette-M...</td> <td>[0, 0, 0, 0, 7, 8, 8, 8, 0]</td> <td>[9, 2, 0, 1, 10, 12, 12, 10, 1]</td> </tr> </tbody> </table> ### Data Splits There are three splits: `train`, `validation` and `test`. \| Config \| Split \| Total \| \| :---------\|-------------:\|-------:\| \| `bokmaal` \| `train` \| 15696 \| \| `bokmaal` \| `validation` \| 2410 \| \| `bokmaal` \| `test` \| 1939 \| \| `nynorsk` \| `train` \| 14174 \| \| `nynorsk` \| `validation` \| 1890 \| \| `nynorsk` \| `test` \| 1511 \| \| `combined`\| `test` \| 29870 \| \| `combined`\| `validation` \| 4300 \| \| `combined`\| `test` \| 3450 \| ## Dataset Creation ### Curation Rationale 1. A _name_ in this context is close to [Saul Kripke's definition of a name](https://en.wikipedia.org/wiki/Saul_Kripke#Naming_and_Necessity), in that a name has a unique reference and its meaning is constant (there are exceptions in the annotations, e.g. "Regjeringen" (en. "Government")). 2. It is the usage of a name that determines the entity type, not the default/literal sense of the name, 3. If there is an ambiguity in the type/sense of a name, then the the default/literal sense of the name is chosen (following [Markert and Nissim, 2002](http://www.lrec-conf.org/proceedings/lrec2002/pdf/11.pdf)). For more details, see the "Annotation Guidelines.pdf" distributed with the corpus. ### Source Data Data was collected using blogs and newspapers in Norwegian, as well as parliament speeches and governamental reports. #### Initial Data Collection and Normalization The texts in the Norwegian Dependency Treebank (NDT) are manually annotated with morphological features, syntactic functions and hierarchical structure. The formalism used for the syntactic annotation is dependency grammar. The treebanks consists of two parts, one part in Norwegian Bokmål (`nob`) and one part in Norwegian Nynorsk (`nno`). Both parts contain around 300.000 tokens, and are a mix of different non-fictional genres. See the [NDT webpage](https://www.nb.no/sprakbanken/show?serial=sbr-10) for more details. ### Annotations The following types of entities are annotated: - Person (`PER`): Real or fictional characters and animals - Organization (`ORG`): Any collection of people, such as firms, institutions, organizations, music groups, sports teams, unions, political parties etc. - Location (`LOC`): Geographical places, buildings and facilities - Geo-political entity (`GPE`): Geographical regions defined by political and/or social groups. A GPE entity subsumes and does not distinguish between a nation, its region, its government, or its people - Product (`PROD`): Artificially produced entities are regarded products. This may include more abstract entities, such as speeches, radio shows, programming languages, contracts, laws and ideas. - Event (`EVT`): Festivals, cultural events, sports events, weather phenomena, wars, etc. Events are bounded in time and space. - Derived (`DRV`): Words (and phrases?) that are dervied from a name, but not a name in themselves. They typically contain a full name and are capitalized, but are not proper nouns. Examples (fictive) are "Brann-treneren" ("the Brann coach") or "Oslo-mannen" ("the man from Oslo"). - Miscellaneous (`MISC`): Names that do not belong in the other categories. Examples are animals species and names of medical conditions. Entities that are manufactured or produced are of type Products, whereas thing naturally or spontaneously occurring are of type Miscellaneous. Furthermore, all `GPE` entities are additionally sub-categorized as being either `ORG` or `LOC`, with the two annotation levels separated by an underscore: - `GPE_LOC`: Geo-political entity, with a locative sense (e.g. "John lives in _Spain_") - `GPE_ORG`: Geo-political entity, with an organisation sense (e.g. "_Spain_ declined to meet with Belgium") The two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. This means that the following sets of entity types can be derived: - 7 types, deleting `_GPE`: `ORG`, `LOC`, `PER`, `PROD`, `EVT`, `DRV`, `MISC` - 8 types, deleting `LOC_` and `ORG_`: `ORG`, `LOC`, `GPE`, `PER`, `PROD`, `EVT`, `DRV`, `MISC` - 9 types, keeping all types: `ORG`, `LOC`, `GPE_LOC`, `GPE_ORG`, `PER`, `PROD`, `EVT`, `DRV`, `MISC` The class distribution is as follows, broken down across the data splits of the UD version of NDT, and sorted by total counts (i.e. the number of examples, not tokens within the spans of the annotatons): \| Type \| Train \| Dev \| Test \| Total \| \| :--------\|-------:\|-------:\|-------:\|-------:\| \| `PER` \| 4033 \| 607 \| 560 \| 5200 \| \| `ORG` \| 2828 \| 400 \| 283 \| 3511 \| \| `GPE_LOC`\| 2132 \| 258 \| 257 \| 2647 \| \| `PROD` \| 671 \| 162 \| 71 \| 904 \| \| `LOC` \| 613 \| 109 \| 103 \| 825 \| \| `GPE_ORG`\| 388 \| 55 \| 50 \| 493 \| \| `DRV` \| 519 \| 77 \| 48 \| 644 \| \| `EVT` \| 131 \| 9 \| 5 \| 145 \| \| `MISC` \| 8 \| 0 \| 0 \| 0 \| To access these reduced versions of the dataset, you can use the configs `bokmaal-7`, `nynorsk-7`, `combined-7` for the NER tag set with 7 tags ( `ORG`, `LOC`, `PER`, `PROD`, `EVT`, `DRV`, `MISC`), and `bokmaal-8`, `nynorsk-8`, `combined-8` for the NER tag set with 8 tags (`LOC_` and `ORG_`: `ORG`, `LOC`, `GPE`, `PER`, `PROD`, `EVT`, `DRV`, `MISC`). By default, the full set (9 tags) will be used. ## Additional Information ### Dataset Curators NorNE was created as a collaboration between [Schibsted Media Group](https://schibsted.com/), [Språkbanken](https://www.nb.no/forskning/sprakbanken/) at the [National Library of Norway](https://www.nb.no) and the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) at the University of Oslo. NorNE was added to 🤗 Datasets by the AI-Lab at the National Library of Norway. ### Licensing Information The NorNE corpus is published under the same [license](https://github.com/ltgoslo/norne/blob/master/LICENSE_NDT.txt) as the Norwegian Dependency Treebank ### Citation Information This dataset is described in the paper _NorNE: Annotating Named Entities for Norwegian_ by Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal, accepted for LREC 2020 and available as pre-print here: https://arxiv.org/abs/1911.12146. ```bibtex @inproceedings{johansen2019ner, title={NorNE: Annotating Named Entities for Norwegian}, author={Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal}, booktitle={LREC 2020}, year={2020}, url={https://arxiv.org/abs/1911.12146} } ``` ### Contributions Thanks to [@versae](https://github.com/versae) for adding this dataset.	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weibo_ner	null	2023-01-25T15:02:04Z	305	6	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:zh", "license:unknown", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - found language: - zh license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: weibo-ner pretty_name: Weibo NER dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-GPE.NAM '1': B-GPE.NOM '2': B-LOC.NAM '3': B-LOC.NOM '4': B-ORG.NAM '5': B-ORG.NOM '6': B-PER.NAM '7': B-PER.NOM '8': I-GPE.NAM '9': I-GPE.NOM '10': I-LOC.NAM '11': I-LOC.NOM '12': I-ORG.NAM '13': I-ORG.NOM '14': I-PER.NAM '15': I-PER.NOM '16': O splits: - name: train num_bytes: 1179589 num_examples: 1350 - name: validation num_bytes: 232380 num_examples: 270 - name: test num_bytes: 237407 num_examples: 270 download_size: 750687 dataset_size: 1649376 train-eval-index: - config: default task: token-classification task_id: entity_extraction splits: train_split: train eval_split: test col_mapping: tokens: tokens ner_tags: tags metrics: - type: seqeval name: seqeval --- # Dataset Card for "Weibo NER" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: None - Repository: https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/Weibo - Paper: [More Information Needed] - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.	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GEM/cochrane-simplification	GEM	2022-10-24T15:30:10Z	305	4	[ "task_categories:text2text-generation", "task_ids:text-simplification", "annotations_creators:none", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]	[ "text2text-generation" ]	2022-03-02T23:29:22Z	--- annotations_creators: - none language_creators: - unknown language: - en license: - cc-by-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - text2text-generation task_ids: - text-simplification pretty_name: cochrane-simplification --- # Dataset Card for GEM/cochrane-simplification ## Dataset Description - Homepage: https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts - Repository: https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts - Paper: https://aclanthology.org/2021.naacl-main.395/ - Leaderboard: N/A - Point of Contact: Ashwin Devaraj ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/cochrane-simplification). ### Dataset Summary Cochrane is an English dataset for paragraph-level simplification of medical texts. Cochrane is a database of systematic reviews of clinical questions, many of which have summaries in plain English targeting readers without a university education. The dataset comprises about 4,500 of such pairs. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/cochrane-simplification') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/cochrane-simplification). #### website [Link](https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts) #### paper [Link](https://aclanthology.org/2021.naacl-main.395/) #### authors Ashwin Devaraj (The University of Texas at Austin), Iain J. Marshall (King's College London), Byron C. Wallace (Northeastern University), Junyi Jessy Li (The University of Texas at Austin) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Link](https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Link](https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [Link](https://aclanthology.org/2021.naacl-main.395/) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{devaraj-etal-2021-paragraph, title = "Paragraph-level Simplification of Medical Texts", author = "Devaraj, Ashwin and Marshall, Iain and Wallace, Byron and Li, Junyi Jessy", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.395", doi = "10.18653/v1/2021.naacl-main.395", pages = "4972--4984", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Ashwin Devaraj #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> [email protected] #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-4.0: Creative Commons Attribution 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> The intended use of this dataset is to train models that simplify medical text at the paragraph level so that it may be more accessible to the lay reader. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Simplification #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> A model trained on this dataset can be used to simplify medical texts to make them more accessible to readers without medical expertise. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> The University of Texas at Austin, King's College London, Northeastern University #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Ashwin Devaraj (The University of Texas at Austin), Iain J. Marshall (King's College London), Byron C. Wallace (Northeastern University), Junyi Jessy Li (The University of Texas at Austin) #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> National Institutes of Health (NIH) grant R01-LM012086, National Science Foundation (NSF) grant IIS-1850153, Texas Advanced Computing Center (TACC) computational resources #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Ashwin Devaraj (The University of Texas at Austin) ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - `gem_id`: string, a unique identifier for the example - `doi`: string, DOI identifier for the Cochrane review from which the example was generated - `source`: string, an excerpt from an abstract of a Cochrane review - `target`: string, an excerpt from the plain-language summary of a Cochrane review that roughly aligns with the source text #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "gem_id": "gem-cochrane-simplification-train-766", "doi": "10.1002/14651858.CD002173.pub2", "source": "Of 3500 titles retrieved from the literature, 24 papers reporting on 23 studies could be included in the review. The studies were published between 1970 and 1997 and together included 1026 participants. Most were cross-over studies. Few studies provided sufficient information to judge the concealment of allocation. Four studies provided results for the percentage of symptom-free days. Pooling the results did not reveal a statistically significant difference between sodium cromoglycate and placebo. For the other pooled outcomes, most of the symptom-related outcomes and bronchodilator use showed statistically significant results, but treatment effects were small. Considering the confidence intervals of the outcome measures, a clinically relevant effect of sodium cromoglycate cannot be excluded. The funnel plot showed an under-representation of small studies with negative results, suggesting publication bias. There is insufficient evidence to be sure about the efficacy of sodium cromoglycate over placebo. Publication bias is likely to have overestimated the beneficial effects of sodium cromoglycate as maintenance therapy in childhood asthma.", "target": "In this review we aimed to determine whether there is evidence for the effectiveness of inhaled sodium cromoglycate as maintenance treatment in children with chronic asthma. Most of the studies were carried out in small groups of patients. Furthermore, we suspect that not all studies undertaken have been published. The results show that there is insufficient evidence to be sure about the beneficial effect of sodium cromoglycate compared to placebo. However, for several outcome measures the results favoured sodium cromoglycate." } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> - `train`: 3568 examples - `validation`: 411 examples - `test`: 480 examples ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> This dataset is the first paragraph-level simplification dataset published (as prior work had primarily focused on simplifying individual sentences). Furthermore, this dataset is in the medical domain, which is an especially useful domain for text simplification. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> no #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon. ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> no #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon. #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `Other: Other Metrics`, `BLEU` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> SARI measures the quality of text simplification #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> The paper which introduced this dataset trained BART models (pretrained on XSum) with unlikelihood training to produce simplification models achieving maximum SARI and BLEU scores of 40 and 43 respectively. ## Dataset Curation ### Original Curation #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> no ### Language Data #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> not validated #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> not filtered ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> no ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> yes/very likely #### Any PII Identification? <!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? --> <!-- scope: periscope --> no identification ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> yes #### Details on how Dataset Addresses the Needs <!-- info: Describe how this dataset addresses the needs of underserved communities. --> <!-- scope: microscope --> This dataset can be used to simplify medical texts that may otherwise be inaccessible to those without medical training. ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> unsure #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> The dataset was generated from abstracts and plain-language summaries of medical literature reviews that were written by medical professionals and thus does was not generated by people representative of the entire English-speaking population. ## Considerations for Using the Data ### PII Risks and Liability ### Licenses ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The main limitation of this dataset is that the information alignment between the abstract and plain-language summary is often rough, so the plain-language summary may contain information that isn't found in the abstract. Furthermore, the plain-language targets often contain formulaic statements like "this evidence is current to [month][year]" not found in the abstracts. Another limitation is that some plain-language summaries do not simplify the technical abstracts very much and still contain medical jargon. #### Unsuited Applications <!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. --> <!-- scope: microscope --> The main pitfall to look out for is errors in factuality. Simplification work so far has not placed a strong emphasis on the logical fidelity of model generations with the input text, and the paper introducing this dataset does not explore modeling techniques to combat this. These kinds of errors are especially pernicious in the medical domain, and the models introduced in the paper do occasionally alter entities like disease and medication names.	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open-llm-leaderboard/details_winglian__llama-2-4b	open-llm-leaderboard	2023-10-26T09:12:35Z	305	0	[ "region:us" ]	null	2023-10-03T14:22:56Z	--- pretty_name: Evaluation run of winglian/llama-2-4b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [winglian/llama-2-4b](https://huggingface.co/winglian/llama-2-4b) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_winglian__llama-2-4b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-26T09:12:20.462280](https://huggingface.co/datasets/open-llm-leaderboard/details_winglian__llama-2-4b/blob/main/results_2023-10-26T09-12-20.462280.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\": 0.0016778523489932886,\n\ \ \"em_stderr\": 0.0004191330178826824,\n \"f1\": 0.039830117449664484,\n\ \ \"f1_stderr\": 0.0011176272810803495,\n \"acc\": 0.2895672680207256,\n\ \ \"acc_stderr\": 0.007874406355324997\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.0004191330178826824,\n\ \ \"f1\": 0.039830117449664484,\n \"f1_stderr\": 0.0011176272810803495\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.004548900682335102,\n \ \ \"acc_stderr\": 0.0018535550440036202\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.574585635359116,\n \"acc_stderr\": 0.013895257666646373\n\ \ }\n}\n```" repo_url: https://huggingface.co/winglian/llama-2-4b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|arc:challenge\|25_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-10-03T14-22-33.156570.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_26T09_12_20.462280 path: - '/details_harness\|drop\|3_2023-10-26T09-12-20.462280.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-26T09-12-20.462280.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_26T09_12_20.462280 path: - '/details_harness\|gsm8k\|5_2023-10-26T09-12-20.462280.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-26T09-12-20.462280.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hellaswag\|10_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T14-22-33.156570.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-management\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T14-22-33.156570.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_03T14_22_33.156570 path: - '/details_harness\|truthfulqa:mc\|0_2023-10-03T14-22-33.156570.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-10-03T14-22-33.156570.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_26T09_12_20.462280 path: - '/details_harness\|winogrande\|5_2023-10-26T09-12-20.462280.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-26T09-12-20.462280.parquet' - config_name: results data_files: - split: 2023_10_03T14_22_33.156570 path: - results_2023-10-03T14-22-33.156570.parquet - split: 2023_10_26T09_12_20.462280 path: - results_2023-10-26T09-12-20.462280.parquet - split: latest path: - results_2023-10-26T09-12-20.462280.parquet --- # Dataset Card for Evaluation run of winglian/llama-2-4b ## Dataset Description - Homepage: - Repository: https://huggingface.co/winglian/llama-2-4b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [winglian/llama-2-4b](https://huggingface.co/winglian/llama-2-4b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_winglian__llama-2-4b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-26T09:12:20.462280](https://huggingface.co/datasets/open-llm-leaderboard/details_winglian__llama-2-4b/blob/main/results_2023-10-26T09-12-20.462280.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": 0.0016778523489932886, "em_stderr": 0.0004191330178826824, "f1": 0.039830117449664484, "f1_stderr": 0.0011176272810803495, "acc": 0.2895672680207256, "acc_stderr": 0.007874406355324997 }, "harness\|drop\|3": { "em": 0.0016778523489932886, "em_stderr": 0.0004191330178826824, "f1": 0.039830117449664484, "f1_stderr": 0.0011176272810803495 }, "harness\|gsm8k\|5": { "acc": 0.004548900682335102, "acc_stderr": 0.0018535550440036202 }, "harness\|winogrande\|5": { "acc": 0.574585635359116, "acc_stderr": 0.013895257666646373 } } ``` ### 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]	[ -0.3643803298473358, -0.5382946133613586, 0.20249880850315094, 0.2916886806488037, -0.16133621335029602, 0.18555857241153717, -0.35962703824043274, -0.24528467655181885, 0.46511995792388916, 0.6070737242698669, -0.7575516104698181, -0.9179380536079407, -0.7014844417572021, 0.19752584397792816, -0.1573353409767151, 1.149470329284668, -0.27327218651771545, -0.2886103391647339, 0.02209162525832653, -0.3656355142593384, -0.2755253314971924, -0.44797152280807495, -0.4876748025417328, 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hmao/reformatted_singleapi_openai	hmao	2023-10-23T23:26:04Z	305	0	[ "region:us" ]	null	2023-10-21T03:43:10Z	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: api_name dtype: string - name: api_definition dtype: string - name: dataset_name dtype: string splits: - name: train num_bytes: 21189 num_examples: 14 download_size: 14297 dataset_size: 21189 --- # Dataset Card for "reformatted_singleapi_openai" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.4557119905948639, -0.1260361522436142, -0.05183250457048416, 0.23945890367031097, -0.2506599724292755, -0.34917545318603516, 0.06383851915597916, 0.00622248649597168, 1.0043108463287354, 0.6087532639503479, -0.9306659698486328, -0.6811484098434448, -0.22342929244041443, -0.26103445887565613, -0.05284275859594345, 1.3726789951324463, -0.19716285169124603, 0.024987997487187386, 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imdb_urdu_reviews	null	2023-01-25T14:32:49Z	304	0	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ur", "license:odbl", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - found language_creators: - machine-generated language: - ur license: - odbl multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: ImDB Urdu Reviews dataset_info: features: - name: sentence dtype: string - name: sentiment dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 114670811 num_examples: 50000 download_size: 31510992 dataset_size: 114670811 --- # Dataset Card for ImDB Urdu Reviews ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/mirfan899/Urdu) - Repository: [Github](https://github.com/mirfan899/Urdu) - Paper: [Aclweb](http://www.aclweb.org/anthology/P11-1015) - Leaderboard: - Point of Contact: [Ikram Ali](https://github.com/akkefa) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - sentence: The movie review which was translated into Urdu. - sentiment: The sentiment exhibited in the review, either positive or negative. ### 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 Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset.	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offenseval2020_tr	null	2023-01-25T14:41:59Z	304	3	[ "task_categories:text-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:tr", "license:cc-by-2.0", "offensive-language-classification", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - found language_creators: - found language: - tr license: - cc-by-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: OffensEval-TR 2020 tags: - offensive-language-classification dataset_info: features: - name: id dtype: int32 - name: tweet dtype: string - name: subtask_a dtype: class_label: names: '0': NOT '1': 'OFF' config_name: offenseval2020-turkish splits: - name: train num_bytes: 4260505 num_examples: 31756 - name: test num_bytes: 481300 num_examples: 3528 download_size: 2048258 dataset_size: 4741805 --- # Dataset Card for OffensEval-TR 2020 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [offensive-turkish](https://coltekin.github.io/offensive-turkish/) - Paper: [A Corpus of Turkish Offensive Language on Social Media](https://coltekin.github.io/offensive-turkish/troff.pdf) - Point of Contact: [Çağrı Çöltekin]([email protected]) ### Dataset Summary The file offenseval-tr-training-v1.tsv contains 31,756 annotated tweets. The file offenseval-annotation.txt contains a short summary of the annotation guidelines. Twitter user mentions were substituted by @USER and URLs have been substitute by URL. Each instance contains up to 1 labels corresponding to one of the following sub-task: - Sub-task A: Offensive language identification; ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://coltekin.github.io/offensive-turkish/troff.pdf). ### Languages The dataset is based on Turkish. ## Dataset Structure ### Data Instances A binary dataset with with (NOT) Not Offensive and (OFF) Offensive tweets. ### Data Fields Instances are included in TSV format as follows: ID INSTANCE SUBA The column names in the file are the following: id tweet subtask_a The labels used in the annotation are listed below. #### Task and Labels (A) Sub-task A: Offensive language identification - (NOT) Not Offensive - This post does not contain offense or profanity. - (OFF) Offensive - This post contains offensive language or a targeted (veiled or direct) offense In our annotation, we label a post as offensive (OFF) if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct. ### Data Splits \| train \| test \| \|------:\|-----:\| \| 31756 \| 3528 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? From tweeter. ### Annotations [More Information Needed] #### Annotation process We describe the labels above in a “flat” manner. However, the annotation process we follow is hierarchical. The following QA pairs give a more flowchart-like procedure to follow 1. Is the tweet in Turkish and understandable? * No: mark tweet X for exclusion, and go to next tweet * Yes: continue to step 2 2. Is the tweet include offensive/inappropriate language? * No: mark the tweet non go to step 4 * Yes: continue to step 3 3. Is the offense in the tweet targeted? * No: mark the tweet prof go to step 4 * Yes: chose one (or more) of grp, ind, oth based on the definitions above. Please try to limit the number of labels unless it is clear that the tweet includes offense against multiple categories. 4. Was the labeling decision difficult (precise answer needs more context, tweets includes irony, or for another reason)? No: go to next tweet * Yes: add the label X, go to next tweet #### 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 The annotations are distributed under the terms of [Creative Commons Attribution License (CC-BY)](https://creativecommons.org/licenses/by/2.0/). Please cite the following paper, if you use this resource. ### Citation Information ``` @inproceedings{coltekin2020lrec, author = {\c{C}\"{o}ltekin, \c{C}a\u{g}r{\i}}, year = {2020}, title = {A Corpus of Turkish Offensive Language on Social Media}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, pages = {6174--6184}, address = {Marseille, France}, url = {https://www.aclweb.org/anthology/2020.lrec-1.758}, } ``` ### Contributions Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset.	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open-llm-leaderboard/details_digitous__Alpacino30b	open-llm-leaderboard	2023-09-17T12:47:36Z	304	0	[ "region:us" ]	null	2023-08-18T11:05:03Z	--- pretty_name: Evaluation run of digitous/Alpacino30b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [digitous/Alpacino30b](https://huggingface.co/digitous/Alpacino30b) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_digitous__Alpacino30b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T12:47:23.707315](https://huggingface.co/datasets/open-llm-leaderboard/details_digitous__Alpacino30b/blob/main/results_2023-09-17T12-47-23.707315.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\": 0.001363255033557047,\n\ \ \"em_stderr\": 0.00037786091964607055,\n \"f1\": 0.06650901845637598,\n\ \ \"f1_stderr\": 0.0014161819077838128,\n \"acc\": 0.47782156604971476,\n\ \ \"acc_stderr\": 0.010661957664554408\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.001363255033557047,\n \"em_stderr\": 0.00037786091964607055,\n\ \ \"f1\": 0.06650901845637598,\n \"f1_stderr\": 0.0014161819077838128\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.15769522365428354,\n \ \ \"acc_stderr\": 0.01003890157506138\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.797947908445146,\n \"acc_stderr\": 0.011285013754047434\n\ \ }\n}\n```" repo_url: https://huggingface.co/digitous/Alpacino30b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|arc:challenge\|25_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T22:46:17.057330.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T12_47_23.707315 path: - '/details_harness\|drop\|3_2023-09-17T12-47-23.707315.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T12-47-23.707315.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T12_47_23.707315 path: - '/details_harness\|gsm8k\|5_2023-09-17T12-47-23.707315.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T12-47-23.707315.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hellaswag\|10_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T22:46:17.057330.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T22:46:17.057330.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T22_46_17.057330 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T22:46:17.057330.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T22:46:17.057330.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T12_47_23.707315 path: - '/details_harness\|winogrande\|5_2023-09-17T12-47-23.707315.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T12-47-23.707315.parquet' - config_name: results data_files: - split: 2023_07_19T22_46_17.057330 path: - results_2023-07-19T22:46:17.057330.parquet - split: 2023_09_17T12_47_23.707315 path: - results_2023-09-17T12-47-23.707315.parquet - split: latest path: - results_2023-09-17T12-47-23.707315.parquet --- # Dataset Card for Evaluation run of digitous/Alpacino30b ## Dataset Description - Homepage: - Repository: https://huggingface.co/digitous/Alpacino30b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [digitous/Alpacino30b](https://huggingface.co/digitous/Alpacino30b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_digitous__Alpacino30b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T12:47:23.707315](https://huggingface.co/datasets/open-llm-leaderboard/details_digitous__Alpacino30b/blob/main/results_2023-09-17T12-47-23.707315.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": 0.001363255033557047, "em_stderr": 0.00037786091964607055, "f1": 0.06650901845637598, "f1_stderr": 0.0014161819077838128, "acc": 0.47782156604971476, "acc_stderr": 0.010661957664554408 }, "harness\|drop\|3": { "em": 0.001363255033557047, "em_stderr": 0.00037786091964607055, "f1": 0.06650901845637598, "f1_stderr": 0.0014161819077838128 }, "harness\|gsm8k\|5": { "acc": 0.15769522365428354, "acc_stderr": 0.01003890157506138 }, "harness\|winogrande\|5": { "acc": 0.797947908445146, "acc_stderr": 0.011285013754047434 } } ``` ### 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]	[ -0.5126112699508667, -0.6670101881027222, 0.21637499332427979, 0.2679614722728729, -0.180175319314003, 0.09367071092128754, -0.40721917152404785, -0.1786535531282425, 0.4781242311000824, 0.5729619860649109, -0.751233696937561, -0.9892656207084656, -0.6622908115386963, 0.18093089759349823, -0.18552786111831665, 1.0964198112487793, -0.2906016707420349, -0.2278759926557541, 0.03258364647626877, -0.3512670397758484, -0.29584887623786926, -0.45365920662879944, -0.5365970730781555, 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open-llm-leaderboard/details_FreedomIntelligence__phoenix-inst-chat-7b	open-llm-leaderboard	2023-09-17T23:22:41Z	304	0	[ "region:us" ]	null	2023-08-18T11:18:08Z	--- pretty_name: Evaluation run of FreedomIntelligence/phoenix-inst-chat-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FreedomIntelligence/phoenix-inst-chat-7b](https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FreedomIntelligence__phoenix-inst-chat-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T23:22:30.864991](https://huggingface.co/datasets/open-llm-leaderboard/details_FreedomIntelligence__phoenix-inst-chat-7b/blob/main/results_2023-09-17T23-22-30.864991.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\": 0.20962667785234898,\n\ \ \"em_stderr\": 0.004168492875886018,\n \"f1\": 0.26621015100671175,\n\ \ \"f1_stderr\": 0.0042103390325487,\n \"acc\": 0.32057213705582843,\n\ \ \"acc_stderr\": 0.00834460377574627\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.20962667785234898,\n \"em_stderr\": 0.004168492875886018,\n\ \ \"f1\": 0.26621015100671175,\n \"f1_stderr\": 0.0042103390325487\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.01288855193328279,\n \ \ \"acc_stderr\": 0.003106901266499664\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6282557221783741,\n \"acc_stderr\": 0.013582306284992875\n\ \ }\n}\n```" repo_url: https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|arc:challenge\|25_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-16T11:30:37.977923.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T23_22_30.864991 path: - '/details_harness\|drop\|3_2023-09-17T23-22-30.864991.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T23-22-30.864991.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T23_22_30.864991 path: - '/details_harness\|gsm8k\|5_2023-09-17T23-22-30.864991.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T23-22-30.864991.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hellaswag\|10_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-16T11:30:37.977923.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-16T11:30:37.977923.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_16T11_30_37.977923 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-16T11:30:37.977923.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-16T11:30:37.977923.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T23_22_30.864991 path: - '/details_harness\|winogrande\|5_2023-09-17T23-22-30.864991.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T23-22-30.864991.parquet' - config_name: results data_files: - split: 2023_08_16T11_30_37.977923 path: - results_2023-08-16T11:30:37.977923.parquet - split: 2023_09_17T23_22_30.864991 path: - results_2023-09-17T23-22-30.864991.parquet - split: latest path: - results_2023-09-17T23-22-30.864991.parquet --- # Dataset Card for Evaluation run of FreedomIntelligence/phoenix-inst-chat-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [FreedomIntelligence/phoenix-inst-chat-7b](https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_FreedomIntelligence__phoenix-inst-chat-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T23:22:30.864991](https://huggingface.co/datasets/open-llm-leaderboard/details_FreedomIntelligence__phoenix-inst-chat-7b/blob/main/results_2023-09-17T23-22-30.864991.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": 0.20962667785234898, "em_stderr": 0.004168492875886018, "f1": 0.26621015100671175, "f1_stderr": 0.0042103390325487, "acc": 0.32057213705582843, "acc_stderr": 0.00834460377574627 }, "harness\|drop\|3": { "em": 0.20962667785234898, "em_stderr": 0.004168492875886018, "f1": 0.26621015100671175, "f1_stderr": 0.0042103390325487 }, "harness\|gsm8k\|5": { "acc": 0.01288855193328279, "acc_stderr": 0.003106901266499664 }, "harness\|winogrande\|5": { "acc": 0.6282557221783741, "acc_stderr": 0.013582306284992875 } } ``` ### 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]	[ -0.4363884925842285, -0.6153749823570251, 0.18971091508865356, 0.24516499042510986, -0.1777373105287552, 0.15027190744876862, -0.40818944573402405, -0.14332780241966248, 0.30467596650123596, 0.5370231866836548, -0.8148638010025024, -0.992912769317627, -0.7383506894111633, 0.16983510553836823, -0.25124359130859375, 1.0523924827575684, -0.05159423127770424, -0.23413942754268646, 0.04887580871582031, -0.32860133051872253, -0.3137972354888916, -0.44002804160118103, -0.5307481288909912, 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open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay	open-llm-leaderboard	2023-09-17T05:58:03Z	304	0	[ "region:us" ]	null	2023-08-18T11:38:24Z	--- pretty_name: Evaluation run of anhnv125/pygmalion-6b-roleplay dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [anhnv125/pygmalion-6b-roleplay](https://huggingface.co/anhnv125/pygmalion-6b-roleplay)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T05:57:52.375499](https://huggingface.co/datasets/open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay/blob/main/results_2023-09-17T05-57-52.375499.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\": 0.002936241610738255,\n\ \ \"em_stderr\": 0.0005541113054709947,\n \"f1\": 0.05561765939597344,\n\ \ \"f1_stderr\": 0.0013547337231371388,\n \"acc\": 0.3190247209594698,\n\ \ \"acc_stderr\": 0.008257334480912119\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709947,\n\ \ \"f1\": 0.05561765939597344,\n \"f1_stderr\": 0.0013547337231371388\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.011372251705837756,\n \ \ \"acc_stderr\": 0.002920666198788722\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6266771902131019,\n \"acc_stderr\": 0.013594002763035516\n\ \ }\n}\n```" repo_url: https://huggingface.co/anhnv125/pygmalion-6b-roleplay leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|arc:challenge\|25_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-01T14:17:43.702617.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T05_57_52.375499 path: - '/details_harness\|drop\|3_2023-09-17T05-57-52.375499.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T05-57-52.375499.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T05_57_52.375499 path: - '/details_harness\|gsm8k\|5_2023-09-17T05-57-52.375499.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T05-57-52.375499.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hellaswag\|10_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-01T14:17:43.702617.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-01T14:17:43.702617.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_01T14_17_43.702617 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-01T14:17:43.702617.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-01T14:17:43.702617.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T05_57_52.375499 path: - '/details_harness\|winogrande\|5_2023-09-17T05-57-52.375499.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T05-57-52.375499.parquet' - config_name: results data_files: - split: 2023_08_01T14_17_43.702617 path: - results_2023-08-01T14:17:43.702617.parquet - split: 2023_09_17T05_57_52.375499 path: - results_2023-09-17T05-57-52.375499.parquet - split: latest path: - results_2023-09-17T05-57-52.375499.parquet --- # Dataset Card for Evaluation run of anhnv125/pygmalion-6b-roleplay ## Dataset Description - Homepage: - Repository: https://huggingface.co/anhnv125/pygmalion-6b-roleplay - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [anhnv125/pygmalion-6b-roleplay](https://huggingface.co/anhnv125/pygmalion-6b-roleplay) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T05:57:52.375499](https://huggingface.co/datasets/open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay/blob/main/results_2023-09-17T05-57-52.375499.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": 0.002936241610738255, "em_stderr": 0.0005541113054709947, "f1": 0.05561765939597344, "f1_stderr": 0.0013547337231371388, "acc": 0.3190247209594698, "acc_stderr": 0.008257334480912119 }, "harness\|drop\|3": { "em": 0.002936241610738255, "em_stderr": 0.0005541113054709947, "f1": 0.05561765939597344, "f1_stderr": 0.0013547337231371388 }, "harness\|gsm8k\|5": { "acc": 0.011372251705837756, "acc_stderr": 0.002920666198788722 }, "harness\|winogrande\|5": { "acc": 0.6266771902131019, "acc_stderr": 0.013594002763035516 } } ``` ### 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]	[ -0.321041464805603, -0.6379930377006531, 0.08170712739229202, 0.22006236016750336, -0.11899030953645706, 0.05371663346886635, -0.4248872995376587, -0.20133811235427856, 0.36492234468460083, 0.48797523975372314, -0.7527934312820435, -0.8815179467201233, -0.6800103187561035, 0.15493635833263397, -0.09689756482839584, 1.100461483001709, -0.2223372608423233, -0.24840034544467926, -0.04752305522561073, -0.28579413890838623, -0.3241922855377197, -0.4304684102535248, -0.4962582588195801, 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muchocine	null	2023-01-25T14:40:54Z	303	4	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:es", "license:unknown", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - found language_creators: - found language: - es license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Muchocine dataset_info: features: - name: review_body dtype: string - name: review_summary dtype: string - name: star_rating dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' splits: - name: train num_bytes: 11871095 num_examples: 3872 download_size: 55556703 dataset_size: 11871095 --- # Dataset Card for Muchocine ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://www.lsi.us.es/~fermin/index.php/Datasets ### Dataset Summary The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language, each with a shorter summary review, and a rating on a 1-5 scale. ### Supported Tasks and Leaderboards - `text-classification`: This dataset can be used for Text Classification, more precisely Sentiment Classification where the task is to predict the `star_rating` for a `reveiw_body` or a `review summaray`. ### Languages Spanish. ## Dataset Structure ### Data Instances An example from the train split: ``` { 'review_body': 'Zoom nos cuenta la historia de Jack Shepard, anteriormente conocido como el Capitán Zoom, Superhéroe que perdió sus poderes y que actualmente vive en el olvido. La llegada de una amenaza para la Tierra hará que la agencia del gobierno que se ocupa de estos temas acuda a él para que entrene a un grupo de jóvenes con poderes para combatir esta amenaza.Zoom es una comedia familiar, con todo lo que eso implica, es decir, guión flojo y previsible, bromas no salidas de tono, historia amorosa de por medio y un desenlace tópico. La gracia está en que los protagonistas son jóvenes con superpoderes, una producción cargada de efectos especiales y unos cuantos guiños frikis. La película además se pasa volando ya que dura poco mas de ochenta minutos y cabe destacar su prologo en forma de dibujos de comics explicando la historia de la cual partimos en la película.Tim Allen protagoniza la cinta al lado de un envejecido Chevy Chase, que hace de doctor encargado del proyecto, un papel bastante gracioso y ridículo, pero sin duda el mejor papel es el de Courteney Cox, en la piel de una científica amante de los comics y de lo más friki. Del grupito de los cuatro niños sin duda la mas graciosa es la niña pequeña con súper fuerza y la que provocara la mayor parte de los gags debido a su poder.Una comedia entretenida y poca cosa más para ver una tarde de domingo. ', 'review_summary': 'Una comedia entretenida y poca cosa más para ver una tarde de domingo ', 'star_rating': 2 } ``` ### Data Fields - `review_body` - longform review - `review_summary` - shorter-form review - `star_rating` - an integer star rating (1-5) The original source also includes part-of-speech tagging for body and summary fields. ### Data Splits One split (train) with 3,872 reviews. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Data was collected from www.muchocine.net and uploaded by Dr. Fermín L. Cruz Mata of La Universidad de Sevilla. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The text reviews and star ratings came directly from users, so no additional annotation was 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 Dr. Fermín L. Cruz Mata. ### Licensing Information [More Information Needed] ### Citation Information See http://www.lsi.us.es/~fermin/index.php/Datasets ### Contributions Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset.	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open-llm-leaderboard/details_victor123__WizardLM-13B-1.0	open-llm-leaderboard	2023-12-03T00:25:04Z	303	0	[ "region:us" ]	null	2023-08-18T00:07:05Z	--- pretty_name: Evaluation run of victor123/WizardLM-13B-1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [victor123/WizardLM-13B-1.0](https://huggingface.co/victor123/WizardLM-13B-1.0)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_victor123__WizardLM-13B-1.0\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-03T00:24:56.534385](https://huggingface.co/datasets/open-llm-leaderboard/details_victor123__WizardLM-13B-1.0/blob/main/results_2023-12-03T00-24-56.534385.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.0,\n \"\ acc_stderr\": 0.0\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \ \ \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/victor123/WizardLM-13B-1.0 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|arc:challenge\|25_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-18T16:18:26.905087.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_18T22_57_01.663121 path: - '/details_harness\|drop\|3_2023-09-18T22-57-01.663121.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-18T22-57-01.663121.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_18T22_57_01.663121 path: - '/details_harness\|gsm8k\|5_2023-09-18T22-57-01.663121.parquet' - split: 2023_12_03T00_24_56.534385 path: - '/details_harness\|gsm8k\|5_2023-12-03T00-24-56.534385.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-03T00-24-56.534385.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hellaswag\|10_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T16:18:26.905087.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T16:18:26.905087.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_18T16_18_26.905087 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-18T16:18:26.905087.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-18T16:18:26.905087.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_18T22_57_01.663121 path: - '/details_harness\|winogrande\|5_2023-09-18T22-57-01.663121.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-18T22-57-01.663121.parquet' - config_name: results data_files: - split: 2023_07_18T16_18_26.905087 path: - results_2023-07-18T16:18:26.905087.parquet - split: 2023_09_18T22_57_01.663121 path: - results_2023-09-18T22-57-01.663121.parquet - split: 2023_12_03T00_24_56.534385 path: - results_2023-12-03T00-24-56.534385.parquet - split: latest path: - results_2023-12-03T00-24-56.534385.parquet --- # Dataset Card for Evaluation run of victor123/WizardLM-13B-1.0 ## Dataset Description - Homepage: - Repository: https://huggingface.co/victor123/WizardLM-13B-1.0 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [victor123/WizardLM-13B-1.0](https://huggingface.co/victor123/WizardLM-13B-1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_victor123__WizardLM-13B-1.0", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-03T00:24:56.534385](https://huggingface.co/datasets/open-llm-leaderboard/details_victor123__WizardLM-13B-1.0/blob/main/results_2023-12-03T00-24-56.534385.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### 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]	[ -0.44117602705955505, -0.6527570486068726, 0.13201111555099487, 0.2557123899459839, -0.11874859035015106, 0.006520054768770933, -0.26635637879371643, -0.14329893887043, 0.316523015499115, 0.6990624070167542, -0.8097339272499084, -0.952201783657074, -0.6397125124931335, 0.232155904173851, -0.24660000205039978, 1.2279093265533447, -0.24115410447120667, -0.045667149126529694, -0.1049153208732605, -0.21818606555461884, -0.45278123021125793, -0.46759626269340515, -0.46885013580322266, 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open-llm-leaderboard/details_mrm8488__llama-2-coder-7b	open-llm-leaderboard	2023-09-17T16:01:59Z	303	0	[ "region:us" ]	null	2023-08-18T11:10:59Z	--- pretty_name: Evaluation run of mrm8488/llama-2-coder-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mrm8488/llama-2-coder-7b](https://huggingface.co/mrm8488/llama-2-coder-7b) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mrm8488__llama-2-coder-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T16:01:46.788467](https://huggingface.co/datasets/open-llm-leaderboard/details_mrm8488__llama-2-coder-7b/blob/main/results_2023-09-17T16-01-46.788467.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\": 0.0003630560893118953,\n \"f1\": 0.059393875838926136,\n\ \ \"f1_stderr\": 0.0013484598070009264,\n \"acc\": 0.4129021950450372,\n\ \ \"acc_stderr\": 0.009590867532569068\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.0003630560893118953,\n\ \ \"f1\": 0.059393875838926136,\n \"f1_stderr\": 0.0013484598070009264\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0712661106899166,\n \ \ \"acc_stderr\": 0.007086462127954491\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7545382794001578,\n \"acc_stderr\": 0.012095272937183644\n\ \ }\n}\n```" repo_url: https://huggingface.co/mrm8488/llama-2-coder-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|arc:challenge\|25_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-09T22:53:49.395953.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T16_01_46.788467 path: - '/details_harness\|drop\|3_2023-09-17T16-01-46.788467.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T16-01-46.788467.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T16_01_46.788467 path: - '/details_harness\|gsm8k\|5_2023-09-17T16-01-46.788467.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T16-01-46.788467.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hellaswag\|10_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T22:53:49.395953.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T22:53:49.395953.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T22_53_49.395953 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T22:53:49.395953.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T22:53:49.395953.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T16_01_46.788467 path: - '/details_harness\|winogrande\|5_2023-09-17T16-01-46.788467.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T16-01-46.788467.parquet' - config_name: results data_files: - split: 2023_08_09T22_53_49.395953 path: - results_2023-08-09T22:53:49.395953.parquet - split: 2023_09_17T16_01_46.788467 path: - results_2023-09-17T16-01-46.788467.parquet - split: latest path: - results_2023-09-17T16-01-46.788467.parquet --- # Dataset Card for Evaluation run of mrm8488/llama-2-coder-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/mrm8488/llama-2-coder-7b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [mrm8488/llama-2-coder-7b](https://huggingface.co/mrm8488/llama-2-coder-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_mrm8488__llama-2-coder-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T16:01:46.788467](https://huggingface.co/datasets/open-llm-leaderboard/details_mrm8488__llama-2-coder-7b/blob/main/results_2023-09-17T16-01-46.788467.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": 0.0003630560893118953, "f1": 0.059393875838926136, "f1_stderr": 0.0013484598070009264, "acc": 0.4129021950450372, "acc_stderr": 0.009590867532569068 }, "harness\|drop\|3": { "em": 0.0012583892617449664, "em_stderr": 0.0003630560893118953, "f1": 0.059393875838926136, "f1_stderr": 0.0013484598070009264 }, "harness\|gsm8k\|5": { "acc": 0.0712661106899166, "acc_stderr": 0.007086462127954491 }, "harness\|winogrande\|5": { "acc": 0.7545382794001578, "acc_stderr": 0.012095272937183644 } } ``` ### 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]	[ -0.3933560848236084, -0.5461770296096802, 0.19502750039100647, 0.2411727011203766, -0.2388528734445572, 0.2246972620487213, -0.356020450592041, -0.22602681815624237, 0.40358445048332214, 0.5522602200508118, -0.6837018132209778, -0.8895406126976013, -0.7819751501083374, 0.25094929337501526, -0.1512642204761505, 1.1564217805862427, -0.2583465576171875, -0.27236104011535645, 0.0006476228008978069, -0.3006080985069275, -0.43230172991752625, -0.49363502860069275, -0.43283262848854065, 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open-llm-leaderboard/details_bhenrym14__airophin-13b-pntk-16k-fp16	open-llm-leaderboard	2023-09-22T13:51:03Z	303	0	[ "region:us" ]	null	2023-08-18T11:17:42Z	--- pretty_name: Evaluation run of bhenrym14/airophin-13b-pntk-16k-fp16 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [bhenrym14/airophin-13b-pntk-16k-fp16](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-fp16)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bhenrym14__airophin-13b-pntk-16k-fp16\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T13:50:50.012213](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__airophin-13b-pntk-16k-fp16/blob/main/results_2023-09-22T13-50-50.012213.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\": 0.12793624161073824,\n\ \ \"em_stderr\": 0.003420665162123249,\n \"f1\": 0.20498217281879216,\n\ \ \"f1_stderr\": 0.0035591754960457413,\n \"acc\": 0.421002792649235,\n\ \ \"acc_stderr\": 0.009731603620470694\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.12793624161073824,\n \"em_stderr\": 0.003420665162123249,\n\ \ \"f1\": 0.20498217281879216,\n \"f1_stderr\": 0.0035591754960457413\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0803639120545868,\n \ \ \"acc_stderr\": 0.007488258573239077\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.01197494866770231\n\ \ }\n}\n```" repo_url: https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-fp16 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|arc:challenge\|25_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-09T13:13:26.207427.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_22T13_50_50.012213 path: - '/details_harness\|drop\|3_2023-09-22T13-50-50.012213.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-22T13-50-50.012213.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T13_50_50.012213 path: - '/details_harness\|gsm8k\|5_2023-09-22T13-50-50.012213.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-22T13-50-50.012213.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hellaswag\|10_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T13:13:26.207427.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T13:13:26.207427.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T13_13_26.207427 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T13:13:26.207427.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T13:13:26.207427.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T13_50_50.012213 path: - '/details_harness\|winogrande\|5_2023-09-22T13-50-50.012213.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-22T13-50-50.012213.parquet' - config_name: results data_files: - split: 2023_08_09T13_13_26.207427 path: - results_2023-08-09T13:13:26.207427.parquet - split: 2023_09_22T13_50_50.012213 path: - results_2023-09-22T13-50-50.012213.parquet - split: latest path: - results_2023-09-22T13-50-50.012213.parquet --- # Dataset Card for Evaluation run of bhenrym14/airophin-13b-pntk-16k-fp16 ## Dataset Description - Homepage: - Repository: https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-fp16 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [bhenrym14/airophin-13b-pntk-16k-fp16](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_bhenrym14__airophin-13b-pntk-16k-fp16", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T13:50:50.012213](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__airophin-13b-pntk-16k-fp16/blob/main/results_2023-09-22T13-50-50.012213.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": 0.12793624161073824, "em_stderr": 0.003420665162123249, "f1": 0.20498217281879216, "f1_stderr": 0.0035591754960457413, "acc": 0.421002792649235, "acc_stderr": 0.009731603620470694 }, "harness\|drop\|3": { "em": 0.12793624161073824, "em_stderr": 0.003420665162123249, "f1": 0.20498217281879216, "f1_stderr": 0.0035591754960457413 }, "harness\|gsm8k\|5": { "acc": 0.0803639120545868, "acc_stderr": 0.007488258573239077 }, "harness\|winogrande\|5": { "acc": 0.7616416732438832, "acc_stderr": 0.01197494866770231 } } ``` ### 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]	[ -0.38153475522994995, -0.6711077094078064, 0.114601731300354, 0.18586045503616333, -0.13892637193202972, 0.08243545889854431, -0.4762793481349945, -0.22731821238994598, 0.3750573694705963, 0.440425306558609, -0.7336462736129761, -0.8398661017417908, -0.7169495820999146, 0.2176736295223236, -0.13701795041561127, 1.0306421518325806, -0.23950593173503876, -0.250171035528183, 0.1783759593963623, -0.23060476779937744, -0.26017194986343384, -0.4026253819465637, -0.4425138831138611, 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disfl_qa	null	2022-11-18T19:58:47Z	302	1	[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2106.04016", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: 'DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering' size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa dataset_info: features: - name: squad_v2_id dtype: string - name: original question dtype: string - name: disfluent question dtype: string - name: title dtype: string - name: context dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 7712523 num_examples: 7182 - name: test num_bytes: 3865097 num_examples: 3643 - name: validation num_bytes: 1072731 num_examples: 1000 download_size: 48935038 dataset_size: 12650351 --- # Dataset Card for DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Disfl-QA](https://github.com/google-research-datasets/disfl-qa) - Paper: [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://arxiv.org/pdf/2106.04016.pdf) - Point of Contact: [disfl-qa team]([email protected]) ### Dataset Summary Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 ([Rajpurkar et al., 2018](https://www.aclweb.org/anthology/P18-2124/)) dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as a source of distractors. The final dataset consists of ~12k (disfluent question, answer) pairs. Over 90\% of the disfluencies are corrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a major gap between speech and NLP research community. The authors hope the dataset can serve as a benchmark dataset for testing robustness of models against disfluent inputs. The expriments reveal that the state-of-the-art models are brittle when subjected to disfluent inputs from Disfl-QA. Detailed experiments and analyses can be found in the [paper](https://arxiv.org/pdf/2106.04016.pdf). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in English only. ## Dataset Structure ### Data Instances This example was too long and was cropped: ``` { "answers": { "answer_start": [94, 87, 94, 94], "text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"] }, "context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...", "id": "56ddde6b9a695914005b9629", "original question": "When were the Normans in Normandy?", "disfluent question": "From which countries no tell me when were the Normans in Normandy?" "title": "Normans" } ``` ### Data Fields - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `original question`: Original question from SQuAD-v2 (a `string` feature) - `disfluent question`: Disfluent question from Disfl-QA (a `string` feature) - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits Disfl-QA consists of ~12k disfluent questions with the following train/dev/test splits: \| File \| Questions \| \|-----\|-----\| \|train.json \| 7182 \| \|dev.json \| 1000 \| \|test.json \| 3643 \| ## Dataset Creation ### Curation Rationale The research in NLP and speech community has been impeded by the lack of curated datasets containing such disfluencies. The datasets available today are mostly conversational in nature, and span a limited number of very specific domains (e.g., telephone conversations, court proceedings). Furthermore, only a small fraction of the utterances in these datasets contain disfluencies, with a limited and skewed distribution of disfluencies types. In the most popular dataset in the literature, the SWITCHBOARD corpus (Godfrey et al., 1992), only 5.9% of the words are disfluencies (Charniak and Johnson, 2001), of which > 50% are repetitions (Shriberg, 1996), which has been shown to be the relatively simpler form of disfluencies (Zayats et al., 2014; Jamshid Lou et al., 2018; Zayats et al., 2019). To fill this gap, the authors presented DISFL-QA, the first dataset containing contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. ### Source Data #### Initial Data Collection and Normalization DISFL-QA is constructed by asking human raters to insert disfluencies in questions from SQUAD-v2, a popular question answering dataset, using the passage and remaining questions as context. These contextual disfluencies lend naturalness to DISFL-QA, and challenge models relying on shallow matching between question and context to predict an answer. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process Each question associated with the paragraph is sent for a human annotation task to add a contextual disfluency using the paragraph as a source of distractors. Finally, to ensure the quality of the dataset, a subsequent round of human evaluation with an option to re-annotate is conducted. #### 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 Disfl-QA dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @inproceedings{gupta-etal-2021-disflqa, title = "{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}", author = "Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal", booktitle = "Findings of ACL", year = "2021" } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.	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hausa_voa_topics	null	2023-01-25T14:31:55Z	302	0	[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ha", "license:unknown", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - found language: - ha license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - topic-classification pretty_name: Hausa Voa News Topic Classification Dataset (HausaVoaTopics) dataset_info: features: - name: news_title dtype: string - name: label dtype: class_label: names: '0': Africa '1': Health '2': Nigeria '3': Politics '4': World splits: - name: train num_bytes: 144932 num_examples: 2045 - name: validation num_bytes: 20565 num_examples: 290 - name: test num_bytes: 41195 num_examples: 582 download_size: 195824 dataset_size: 206692 --- # Dataset Card for Hausa VOA News Topic Classification dataset (hausa_voa_topics) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - - Repository: https://github.com/uds-lsv/transfer-distant-transformer-african - Paper: https://www.aclweb.org/anthology/2020.emnlp-main.204/ - Leaderboard: - - Point of Contact: Michael A. Hedderich and David Adelani {mhedderich, didelani} (at) lsv.uni-saarland.de ### Dataset Summary A news headline topic classification dataset, similar to AG-news, for Hausa. The news headlines were collected from [VOA Hausa](https://www.voahausa.com/). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Hausa (ISO 639-1: ha) ## Dataset Structure ### Data Instances An instance consists of a news title sentence and the corresponding topic label. ### Data Fields - `news_title`: A news title - `label`: The label describing the topic of the news title. Can be one of the following classes: Nigeria, Africa, World, Health or Politics. ### 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 Thanks to [@michael-aloys](https://github.com/michael-aloys) for adding this dataset.	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msr_text_compression	null	2022-11-18T21:30:29Z	302	3	[ "task_categories:summarization", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended\|other-Open-American-National-Corpus-(OANC1)", "language:en", "license:other", "region:us" ]	[ "summarization" ]	2022-03-02T23:29:22Z	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - other license_details: Microsoft Research Data License Agreement multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended\|other-Open-American-National-Corpus-(OANC1) task_categories: - summarization task_ids: [] pretty_name: MsrTextCompression dataset_info: features: - name: source_id dtype: string - name: domain dtype: string - name: source_text dtype: string - name: targets sequence: - name: compressed_text dtype: string - name: judge_id dtype: string - name: num_ratings dtype: int64 - name: ratings sequence: int64 splits: - name: train num_bytes: 5001312 num_examples: 4936 - name: validation num_bytes: 449691 num_examples: 447 - name: test num_bytes: 804536 num_examples: 785 download_size: 0 dataset_size: 6255539 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://msropendata.com/datasets/f8ce2ec9-7fbd-48f7-a8bb-2d2279373563 - Repository: - Paper: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/09/Sentence_Compression_final-1.pdf - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains sentences and short paragraphs with corresponding shorter (compressed) versions. There are up to five compressions for each input text, together with quality judgements of their meaning preservation and grammaticality. The dataset is derived using source texts from the Open American National Corpus (ww.anc.org) and crowd-sourcing. ### Supported Tasks and Leaderboards Text Summarization ### Languages English ## Dataset Structure ### Data Instances It contains approximately 6,000 source texts with multiple compressions (about 26,000 pairs of source and compressed texts), representing business letters, newswire, journals, and technical documents sampled from the Open American National Corpus (OANC1). - Each source text is accompanied by up to five crowd-sourced rewrites constrained to a preset compression ratio and annotated with quality judgments. Multiple rewrites permit study of the impact of operations on human compression quality and facilitate automatic evaluation. - This dataset is the first to provide compressions at the multi-sentence (two-sentence paragraph) level, which may present a stepping stone to whole document summarization. - Many of these two-sentence paragraphs are compressed both as paragraphs and separately sentence-bysentence, offering data that may yield insights into the impact of multi-sentence operations on human compression quality. \| Description \| Source \| Target \| Average CPS \| Meaning Quality \| Grammar Quality \| \| :------------- \| :----------: \| -----------: \| -----------: \| -----------: \| -----------: \| \| 1-Sentence \| 3764 \| 15523 \| 4.12 \| 2.78 \| 2.81 \| \| 2-Sentence \| 2405 \| 10900 \| 4.53 \| 2.78 \| 2.83 \| Note: Average CPS = Average Compressions per Source Text ### Data Fields ``` {'domain': 'Newswire', 'source_id': '106', 'source_text': '" Except for this small vocal minority, we have just not gotten a lot of groundswell against this from members, " says APA president Philip G. Zimbardo of Stanford University.', 'targets': {'compressed_text': ['"Except for this small vocal minority, we have not gotten a lot of groundswell against this," says APA president Zimbardo.', '"Except for a vocal minority, we haven\'t gotten much groundswell from members, " says Philip G. Zimbardo of Stanford University.', 'APA president of Stanford has stated that except for a vocal minority they have not gotten a lot of pushback from members.', 'APA president Philip G. Zimbardo of Stanford says they have not had much opposition against this.'], 'judge_id': ['2', '22', '10', '0'], 'num_ratings': [3, 3, 3, 3], 'ratings': [[6, 6, 6], [11, 6, 6], [6, 11, 6], [6, 11, 11]]}} ``` - source_id: index of article per original dataset - source_text: uncompressed original text - domain: source of the article - targets: - compressed_text: compressed version of `source_text` - judge_id: anonymized ids of crowdworkers who proposed compression - num_ratings: number of ratings available for each proposed compression - ratings: see table below Ratings system (excerpted from authors' README): - 6 = Most important meaning Flawless language (3 on meaning and 3 on grammar as per the paper's terminology) - 7 = Most important meaning Minor errors (3 on meaning and 2 on grammar) - 9 = Most important meaning Disfluent or incomprehensible (3 on meaning and 1 on grammar) - 11 = Much meaning Flawless language (2 on meaning and 3 on grammar) - 12 = Much meaning Minor errors (2 on meaning and 2 on grammar) - 14 = Much meaning Disfluent or incomprehensible (2 on meaning and 1 on grammar) - 21 = Little or none meaning Flawless language (1 on meaning and 3 on grammar) - 22 = Little or none meaning Minor errors (1 on meaning and 2 on grammar) - 24 = Little or none meaning Disfluent or incomprehensible (1 on meaning and 1 on grammar) See README.txt from data archive for additional details. ### Data Splits There are 4,936 source texts in the training, 448 in the development, and 785 in the test set. ## Dataset Creation ### Annotations #### Annotation process Compressions were created using UHRS, an inhouse crowd-sourcing system similar to Amazon’s Mechanical Turk, in two annotation rounds, one for shortening and a second to rate compression quality: 1. In the first round, five workers were tasked with abridging each source text by at least 25%, while remaining grammatical and fluent, and retaining the meaning of the original. 2. In the second round, 3-5 judges (raters) were asked to evaluate the grammaticality of each compression on a scale from 1 (major errors, disfluent) through 3 (fluent), and again analogously for meaning preservation on a scale from 1 (orthogonal) through 3 (most important meaning-preserving). ## Additional Information ### Licensing Information Microsoft Research Data License Agreement ### Citation Information @inproceedings{Toutanova2016ADA, title={A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs}, author={Kristina Toutanova and Chris Brockett and Ke M. Tran and Saleema Amershi}, booktitle={EMNLP}, year={2016} } ### Contributions Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset.	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hakurei/open-instruct-v1	hakurei	2023-04-17T03:03:13Z	302	87	[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "region:us" ]	[ "text-generation" ]	2023-04-04T23:10:41Z	--- license: apache-2.0 task_categories: - text-generation language: - en size_categories: - 100K<n<1M --- # Open Instruct V1 - A dataset for having LLMs follow instructions. Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training. ## Dataset Breakdown \| Dataset \| Amount of Samples \| \|----------------\|-------------------\| \| [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) \| 51759 \| \| [Self Instruct](https://github.com/yizhongw/self-instruct) \| 82599 \| \| [GPT-4 Instruct](https://github.com/teknium1/GPTeacher) \| 18194 \| \| [Code Alpaca](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) \| 18019 \| \| [Dolly](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) \| 15015 \| \| [Synthetic](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) \| 33143 \| \| [Roleplay](https://github.com/teknium1/GPTeacher) \| 3146 \| \| [asss](https://huggingface.co/datasets/HuggingFaceH4/asss) \| 448 \| \| [instruction-dataset](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) \| 327 \| \| Total \| 222650 \|	[ 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open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3	open-llm-leaderboard	2023-10-21T23:45:34Z	302	0	[ "region:us" ]	null	2023-08-18T11:07:05Z	--- pretty_name: Evaluation run of lmsys/vicuna-7b-v1.3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-21T23:45:21.646720](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3/blob/main/results_2023-10-21T23-45-21.646720.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\": 0.12730704697986578,\n\ \ \"em_stderr\": 0.003413474068983651,\n \"f1\": 0.17891254194630765,\n\ \ \"f1_stderr\": 0.0035073277688968674,\n \"acc\": 0.38083789051163464,\n\ \ \"acc_stderr\": 0.0095991004919272\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.12730704697986578,\n \"em_stderr\": 0.003413474068983651,\n\ \ \"f1\": 0.17891254194630765,\n \"f1_stderr\": 0.0035073277688968674\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.05686125852918878,\n \ \ \"acc_stderr\": 0.0063787902420996325\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7048145224940805,\n \"acc_stderr\": 0.012819410741754765\n\ \ }\n}\n```" repo_url: https://huggingface.co/lmsys/vicuna-7b-v1.3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|arc:challenge\|25_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T16:22:02.219224.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_21T23_45_21.646720 path: - '/details_harness\|drop\|3_2023-10-21T23-45-21.646720.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-21T23-45-21.646720.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_21T23_45_21.646720 path: - '/details_harness\|gsm8k\|5_2023-10-21T23-45-21.646720.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-21T23-45-21.646720.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hellaswag\|10_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T16:22:02.219224.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T16:22:02.219224.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T16_22_02.219224 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T16:22:02.219224.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T16:22:02.219224.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_21T23_45_21.646720 path: - '/details_harness\|winogrande\|5_2023-10-21T23-45-21.646720.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-21T23-45-21.646720.parquet' - config_name: results data_files: - split: 2023_07_19T16_22_02.219224 path: - results_2023-07-19T16:22:02.219224.parquet - split: 2023_10_21T23_45_21.646720 path: - results_2023-10-21T23-45-21.646720.parquet - split: latest path: - results_2023-10-21T23-45-21.646720.parquet --- # Dataset Card for Evaluation run of lmsys/vicuna-7b-v1.3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/lmsys/vicuna-7b-v1.3 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-21T23:45:21.646720](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3/blob/main/results_2023-10-21T23-45-21.646720.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": 0.12730704697986578, "em_stderr": 0.003413474068983651, "f1": 0.17891254194630765, "f1_stderr": 0.0035073277688968674, "acc": 0.38083789051163464, "acc_stderr": 0.0095991004919272 }, "harness\|drop\|3": { "em": 0.12730704697986578, "em_stderr": 0.003413474068983651, "f1": 0.17891254194630765, "f1_stderr": 0.0035073277688968674 }, "harness\|gsm8k\|5": { "acc": 0.05686125852918878, "acc_stderr": 0.0063787902420996325 }, "harness\|winogrande\|5": { "acc": 0.7048145224940805, "acc_stderr": 0.012819410741754765 } } ``` ### 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]	[ -0.37565040588378906, -0.6621671915054321, 0.3153010904788971, 0.2390533834695816, -0.25641098618507385, -0.005156276747584343, -0.31326043605804443, -0.17649997770786285, 0.4418141841888428, 0.5451951622962952, -0.6763996481895447, -0.9673342704772949, -0.6065925359725952, 0.18666483461856842, -0.11479548364877701, 1.0131871700286865, -0.22933699190616608, -0.16628244519233704, 0.043448884040117264, -0.31739720702171326, -0.4587942063808441, -0.48370128870010376, -0.5699666142463684, 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Lin-Chen/ShareGPT4V	Lin-Chen	2023-11-22T15:48:45Z	302	108	[ "task_categories:visual-question-answering", "task_categories:question-answering", "task_categories:conversational", "size_categories:1M<n", "language:en", "license:cc-by-nc-4.0", "arxiv:2311.12793", "region:us" ]	[ "visual-question-answering", "question-answering", "conversational" ]	2023-11-20T09:29:12Z	--- license: cc-by-nc-4.0 task_categories: - visual-question-answering - question-answering - conversational language: - en pretty_name: ShareGPT4V Captions 1.2M Dataset Card size_categories: - 1M<n configs: - config_name: ShareGPT4V data_files: sharegpt4v_instruct_gpt4-vision_cap100k.json - config_name: ShareGPT4V-PT data_files: share-captioner_coco_lcs_sam_1246k_1107.json --- # ShareGPT4V 1.2M Dataset Card ## Dataset details Dataset type: ShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data. It is constructed to enhance modality alignment and fine-grained visual concept perception in Large Multi-Modal Models (LMMs) during both the pre-training and supervised fine-tuning stages. This advancement aims to bring LMMs towards GPT4-Vision capabilities. * sharegpt4v_instruct_gpt4-vision_cap100k.json is generated by GPT4-Vision (ShareGPT4V). * share-captioner_coco_lcs_sam_1246k_1107.json is generated by our Share-Captioner trained on GPT4-Vision-generated data (ShareGPT4V-PT). * sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json is curated from sharegpt4v_instruct_gpt4-vision_cap100k.json for the supervised fine-tuning stage. Dataset date: ShareGPT4V Captions 1.2M was collected in 11.07 2023. Paper or resources for more information: [[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)] License: Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use ## Intended use Primary intended uses: The primary use of ShareGPT4V Captions 1.2M is research on large multimodal models and chatbots. Primary intended users: The primary intended users of this dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.	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mnoukhov/openai_summarize_comparisons_tldrprompt	mnoukhov	2023-11-30T04:11:01Z	302	0	[ "region:us" ]	null	2023-11-27T06:57:39Z	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid1 path: data/valid1-* - split: valid2 path: data/valid2-* dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 156593160 num_examples: 92534 - name: test num_bytes: 142265844 num_examples: 83629 - name: valid1 num_bytes: 56388492 num_examples: 33082 - name: valid2 num_bytes: 85940008 num_examples: 50715 download_size: 61340645 dataset_size: 441187504 --- # Dataset Card for "openai_summarize_comparisons_tldrprompt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5392696261405945, 0.015761513262987137, -0.02683437056839466, 0.15824614465236664, 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dutch_social	null	2023-01-25T14:29:36Z	301	6	[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:multi-label-classification", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:nl", "license:cc-by-nc-4.0", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - en - nl license: - cc-by-nc-4.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - multi-label-classification pretty_name: Dutch Social Media Collection dataset_info: features: - name: full_text dtype: string - name: text_translation dtype: string - name: screen_name dtype: string - name: description dtype: string - name: desc_translation dtype: string - name: location dtype: string - name: weekofyear dtype: int64 - name: weekday dtype: int64 - name: month dtype: int64 - name: year dtype: int64 - name: day dtype: int64 - name: point_info dtype: string - name: point dtype: string - name: latitude dtype: float64 - name: longitude dtype: float64 - name: altitude dtype: float64 - name: province dtype: string - name: hisco_standard dtype: string - name: hisco_code dtype: string - name: industry dtype: bool_ - name: sentiment_pattern dtype: float64 - name: subjective_pattern dtype: float64 - name: label dtype: class_label: names: '0': neg '1': neu '2': pos config_name: dutch_social splits: - name: train num_bytes: 105569586 num_examples: 162805 - name: test num_bytes: 35185351 num_examples: 54268 - name: validation num_bytes: 34334756 num_examples: 54269 download_size: 68740666 dataset_size: 175089693 --- # Dataset Card for Dutch Social Media Collection ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Dutch Social Media Collection](http://datasets.coronawhy.org/dataset.xhtml?persistentId=doi:10.5072/FK2/MTPTL7) - Repository: - Paper: (in-progress) https://doi.org/10.5072/FK2/MTPTL7 - Leaderboard: - Point of Contact: [Aakash Gupta](mailto:[email protected]) ### Dataset Summary The dataset contains 10 files with around 271,342 tweets. The tweets are filtered via the official Twitter API to contain tweets in Dutch language or by users who have specified their location information within Netherlands geographical boundaries. Using natural language processing we have classified the tweets for their HISCO codes. If the user has provided their location within Dutch boundaries, we have also classified them to their respective provinces The objective of this dataset is to make research data available publicly in a FAIR (Findable, Accessible, Interoperable, Reusable) way. Twitter's Terms of Service Licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (2020-10-27) ### Supported Tasks and Leaderboards `sentiment analysis`, `multi-label classification`, `entity-extraction` ### Languages The text is primarily in Dutch with some tweets in English and other languages. The BCP 47 code is `nl` and `en` ## Dataset Structure ### Data Instances An example of the data field will be: ``` { "full_text": "@pflegearzt @Friedelkorn @LAguja44 Pardon, wollte eigentlich das zitieren: \nhttps://t.co/ejO7bIMyj8\nMeine mentions sind inzw komplett undurchschaubar weil da Leute ihren supporterclub zwecks Likes zusammengerufen haben.", "text_translation": "@pflegearzt @Friedelkorn @ LAguja44 Pardon wollte zitieren eigentlich das:\nhttps://t.co/ejO7bIMyj8\nMeine mentions inzw sind komplett undurchschaubar weil da Leute ihren supporter club Zwecks Likes zusammengerufen haben.", "created_at": 1583756789000, "screen_name": "TheoRettich", "description": "I ❤️science, therefore a Commie. ☭ FALGSC: Part of a conspiracy which wants to achieve world domination. Tankie-Cornucopian. Ecology is a myth", "desc_translation": "I ❤️science, Therefore a Commie. ☭ FALGSC: Part of a conspiracy How many followers wants to Achieve World Domination. Tankie-Cornucopian. Ecology is a myth", "weekofyear": 11, "weekday": 0, "day": 9, "month": 3, "year": 2020, "location": "Netherlands", "point_info": "Nederland", "point": "(52.5001698, 5.7480821, 0.0)", "latitude": 52.5001698, "longitude": 5.7480821, "altitude": 0, "province": "Flevoland", "hisco_standard": null, "hisco_code": null, "industry": false, "sentiment_pattern": 0, "subjective_pattern": 0 } ``` ### Data Fields \| Column Name \| Description \| \| --- \| --- \| \| full_text \| Original text in the tweet \| \| text_translation \| English translation of the full text \| \| created_at \| Date of tweet creation \| \| screen_name \| username of the tweet author \| \| description \| description as provided in the users bio \| \| desc_translation \| English translation of user's bio/ description \| \| location \| Location information as provided in the user's bio \| \| weekofyear \| week of the year \| \| weekday \| Day of the week information; Monday=0....Sunday = 6\| \| month \| Month of tweet creation \| \| year \| year of tweet creation \| \| day \| day of tweet creation \| \| point_info \| point information from location columnd \| \| point \| tuple giving lat, lon & altitude information \| \| latitude \| geo-referencing information derived from location data \| \| longitude \| geo-referencing information derived from location data \| \| altitude \| geo-referencing information derived from location data\| \| province \| Province given location data of user \| \| hisco_standard \| HISCO standard key word; if available in tweet \| \| hisco_code\| HISCO standard code as derived from `hisco_standard`\| \| industry \| Whether the tweet talks about industry `(True/False)` \| \| sentiment_score \| Sentiment score -1.0 to 1.0 \| \| subjectivity_score \| Subjectivity scores 0 to 1 \| Missing values are replaced with empty strings or -1 (-100 for missing sentiment_score). ### Data Splits Data has been split into Train: 60%, Validation: 20% and Test: 20% ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The tweets were hydrated using Twitter's API and then filtered for those which were in Dutch language and/or for users who had mentioned that they were from within Netherlands geographical borders. #### Who are the source language producers? The language producers are twitter users who have identified their location within the geographical boundaries of Netherland. Or those who have tweeted in the dutch language! ### Annotations Using Natural language processing, we have classified the tweets on industry and for HSN HISCO codes. Depending on the user's location, their provincial information is also added. Please check the file/column for detailed information. The tweets are also classified on the sentiment & subjectivity scores. Sentiment scores are between -1 to +1 Subjectivity scores are between 0 to 1 #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information As of writing this data card no anonymization has been carried out on the tweets or user data. As such, if the twitter user has shared any personal & sensitive information, then it may be available in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [Aakash Gupta](mailto:[email protected]) Th!nkEvolve Consulting and Researcher at CoronaWhy ### Licensing Information CC BY-NC 4.0 ### Citation Information @data{FK2/MTPTL7_2020, author = {Gupta, Aakash}, publisher = {COVID-19 Data Hub}, title = {{Dutch social media collection}}, year = {2020}, version = {DRAFT VERSION}, doi = {10.5072/FK2/MTPTL7}, url = {https://doi.org/10.5072/FK2/MTPTL7} } ### Contributions Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.	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hover	null	2023-01-25T14:32:26Z	301	1	[ "task_categories:text-retrieval", "task_ids:fact-checking-retrieval", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "arxiv:2011.03088", "region:us" ]	[ "text-retrieval" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - expert-generated - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-retrieval task_ids: - fact-checking-retrieval paperswithcode_id: hover pretty_name: HoVer dataset_info: features: - name: id dtype: int32 - name: uid dtype: string - name: claim dtype: string - name: supporting_facts list: - name: key dtype: string - name: value dtype: int32 - name: label dtype: class_label: names: '0': NOT_SUPPORTED '1': SUPPORTED - name: num_hops dtype: int32 - name: hpqa_id dtype: string splits: - name: train num_bytes: 5532178 num_examples: 18171 - name: validation num_bytes: 1299252 num_examples: 4000 - name: test num_bytes: 927513 num_examples: 4000 download_size: 12257835 dataset_size: 7758943 --- # Dataset Card for HoVer ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://hover-nlp.github.io/ - Repository: https://github.com/hover-nlp/hover - Paper: https://arxiv.org/abs/2011.03088 - Leaderboard: https://hover-nlp.github.io/ - Point of Contact: [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A sample training set is provided below ``` {'id': 14856, 'uid': 'a0cf45ea-b5cd-4c4e-9ffa-73b39ebd78ce', 'claim': 'The park at which Tivolis Koncertsal is located opened on 15 August 1843.', 'supporting_facts': [{'key': 'Tivolis Koncertsal', 'value': 0}, {'key': 'Tivoli Gardens', 'value': 1}], 'label': 'SUPPORTED', 'num_hops': 2, 'hpqa_id': '5abca1a55542993a06baf937'} ``` Please note that in test set sentence only id, uid and claim are available. Labels are not available in test set and are represented by -1. ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.	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inquisitive_qg	null	2022-11-18T20:09:50Z	301	1	[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "question-generation", "region:us" ]	[ "text2text-generation" ]	2022-03-02T23:29:22Z	--- pretty_name: InquisitiveQg annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: inquisitive tags: - question-generation dataset_info: features: - name: id dtype: int32 - name: article_id dtype: int32 - name: article dtype: string - name: sentence_id dtype: int32 - name: sentence dtype: string - name: span dtype: string - name: question dtype: string - name: span_start_position dtype: int32 - name: span_end_position dtype: int32 config_name: plain_text splits: - name: train num_bytes: 66099232 num_examples: 15931 - name: validation num_bytes: 8904329 num_examples: 1991 - name: test num_bytes: 7167203 num_examples: 1894 download_size: 7085941 dataset_size: 82170764 --- # Dataset Card for InquisitiveQg ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Add homepage URL here if available (unless it's a GitHub repository)]() - Repository: [If the dataset is hosted on github or has a github homepage, add URL here]() - Paper: [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.	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kan_hope	null	2023-01-25T14:33:30Z	301	1	[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "language:kn", "license:cc-by-4.0", "hope-speech-detection", "arxiv:2108.04616", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - kn license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification pretty_name: KanHope language_bcp47: - en-IN - kn-IN tags: - hope-speech-detection dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Not-Hope '1': Hope splits: - name: train num_bytes: 494898 num_examples: 4940 - name: test num_bytes: 65722 num_examples: 618 download_size: 568972 dataset_size: 560620 --- # Dataset Card for KanHope ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://zenodo.org/record/4904729 - Repository: [KanHope](https://github.com/adeepH/KanHope) - Paper: [Hope speech detection in Under-resourced Kannada langauge](https://arxiv.org/abs/2108.04616) - Leaderboard: [N/A] - Point of Contact: [Adeep Hande]([email protected]) ### Dataset Summary KanHope dataset is a code-mixed Kannada-English dataset for hope speech detection. All texts are scraped from the comments section of YouTube. The dataset consists of 6,176 user-generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope speech or Not-hope speech. ### Supported Tasks and Leaderboards This task aims to detect Hope speech content of the code-mixed dataset of comments/posts in Dravidian Languages ( Kannada-English) collected from social media. The comment/post may contain more than one sentence, but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. ### Languages Code-mixed text in Dravidian languages (Kannada-English). ## Dataset Structure ### Data Instances An example from the Kannada dataset looks as follows: \| text \| label \| \| :------ \| :----- \| \| �� ͭ� heartly heltidini... plz avrigella namma nimmellara supprt beku \| 0 (Non_hope speech) \| \| Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. \| 1 (Hope Speech) \| ### Data Fields Kannada - `text`: Kannada-English code mixed comment. - `label`: integer from either of 0 or 1 that corresponds to these values: "Non_hope Speech", "Hope Speech" ### Data Splits \| \| train \| validation \| test \| \|---------\|------:\|-----------:\|-----:\| \| Kannada \| 4941 \| 618 \| 617 \| ## Dataset Creation ### Curation Rationale Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums. ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? Youtube users ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @misc{hande2021hope, title={Hope Speech detection in under-resourced Kannada language}, author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi}, year={2021}, eprint={2108.04616}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@adeepH](https://github.com/adeepH) for adding this dataset.	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tunizi	null	2023-01-25T14:54:36Z	301	0	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:aeb", "license:unknown", "arxiv:2004.14303", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - found language: - aeb license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: tunizi pretty_name: TUNIZI dataset_info: features: - name: id dtype: string - name: sentence dtype: string - name: target dtype: class_label: names: '0': '1' '1': '-1' splits: - name: train num_bytes: 211166 num_examples: 3000 download_size: 162781 dataset_size: 211166 --- # Dataset Card for TUNIZI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/chaymafourati/TUNIZI-Sentiment-Analysis-Tunisian-Arabizi-Dataset - Repository: https://github.com/chaymafourati/TUNIZI-Sentiment-Analysis-Tunisian-Arabizi-Dataset - Paper: https://arxiv.org/abs/2004.14303 - Point of Contact: Chayma Fourati ([email protected]) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages This dataset uses Tunisian Arabic written with latin script (BCP-47: aeb-Latn) ## 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.	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urdu_fake_news	null	2023-01-25T15:01:58Z	301	0	[ "task_categories:text-classification", "task_ids:fact-checking", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:ur", "license:unknown", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ur license: - unknown multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking - intent-classification pretty_name: Bend the Truth (Urdu Fake News) dataset_info: features: - name: news dtype: string - name: label dtype: class_label: names: '0': Fake '1': Real - name: category dtype: class_label: names: '0': bus '1': hlth '2': sp '3': tch '4': sbz splits: - name: train num_bytes: 1762905 num_examples: 638 - name: test num_bytes: 799587 num_examples: 262 download_size: 1042653 dataset_size: 2562492 --- # Dataset Card for Bend the Truth (Urdu Fake News) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/) - Repository: [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/) - Paper: - Leaderboard: - Point of Contact: [Maaz Amjad](https://github.com/MaazAmjad) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - news: a string in urdu - label: the label indicating whethere the provided news is real or fake. - category: The intent of the news being presented. The available 5 classes are Sports, Health, Technology, Entertainment, and Business. ### 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 Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset.	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DDSC/partial-danish-gigaword-no-twitter	DDSC	2023-03-13T14:01:53Z	301	4	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:da", "license:cc-by-4.0", "region:us" ]	[ "text-generation" ]	2022-03-02T23:29:22Z	--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - da license: - cc-by-4.0 multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - text-generation task_ids: - language-modeling pretty_name: Danish Gigaword Corpus (no Twitter) language_bcp47: - da - da-bornholm - da-synnejyl --- # Dataset Card for [Danish Gigaword (no Twitter)] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://gigaword.dk - Paper: http://www.derczynski.com/papers/dagw.pdf ### Dataset Summary The Danish Gigaword Corpus contains text spanning several domains and forms. This version does not include the sections containing Tweets. ### Supported Tasks and Leaderboards Pre-training of language models. ### Language Danish ## Dataset Structure The dataset contains text from 23 different sources which are thoroughly defined in [Source Data](#source-data). See the [homepage](https://gigaword.dk) or [paper](http://www.derczynski.com/papers/dagw.pdf) for more information. ### Data Instances Each entry in the dataset consists of a single text with associated metadata ### Data Fields An entry in the dataset consists of the following fields: - `text`(`str`): The content of the document. - `source` (`str`): The source of the document (see [Source Data](#source-data)). - `doc_id` (`str`): An unique identifer for each document. - `LICENSE` (`str`): The license of the document. The licenses vary according to the source. - `uri` (`str`): The uri of the document. Not available for all sources. - `data_built`(`str`): Date the document was built. Not avaialable for all sources. ### Data Splits The entire corpus is provided in the `train` split. ## Dataset Creation ### Source Data Below follows a brief overview of the sources in the corpus along with their individual license. \| Source \| License \| \| ----------------- \| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| adl \| Creative Commons Legal Code 1.0 Universal \| \| botxt \| Creative Commons Legal Code 1.0 Universal \| \| cc \| Creative Commons Legal Code 1.0 Universal \| \| danavis \| Creative Commons Legal Code 1.0 Universal \| \| dannet \| [dannet license](https://cst.ku.dk/projekter/dannet/license.txt) \| \| depbank \| Attribution-ShareAlike 4.0 International \| \| ep \| Creative Commons Legal Code 1.0 Universal \| \| ft \| Creative Commons Legal Code 1.0 Universal \| \| gutenberg \| [gutenberg license](https://www.gutenberg.org/policy/license.html) \| \| hest \| Creative Commons Legal Code 1.0 Universal \| \| jvj \| Attribution-ShareAlike 4.0 International \| \| naat \| Creative Commons Legal Code 1.0 Universal \| \| opensub \| The data set comes with the same license as the original sources. Please, check the information about the source that is given on http://opus.nlpl.eu/OpenSubtitles-v2018.php \| \| relig \| Creative Commons Legal Code 1.0 Universal \| \| retsinformationdk \| Danish Copyright law at https://www.retsinformation.dk/forms/r0710.aspx?id=164796 states "§ 9. Love, administrative forskrifter, retsafgørelser og lignende offentlige aktstykker er ikke genstand for ophavsret. Stk. 2. Bestemmelsen i stk. 1 gælder ikke for værker, der fremtræder som selvstændige bidrag i de i stk. 1 nævnte aktstykker. Sådanne værker må dog gengives i forbindelse med aktstykket. Retten til videre udnyttelse afhænger af de i øvrigt gældende regler." \| \| retspraksis \| Creative Commons Legal Code 1.0 Universal \| \| skat \| Creative Commons Legal Code 1.0 Universal \| \| spont \| Creative Commons Legal Code 1.0 Universal \| \| synne \| Creative Commons Legal Code 1.0 Universal \| \| tv2r \| The owner of this content is TV2 Regionerne, Denmark. Creative Commons Attribution 4.0 International \| \| wiki \| Creative Commons Legal Code 1.0 Universal \| \| wikibooks \| Creative Commons Legal Code 1.0 Universal \| \| wikisource \| Creative Commons Legal Code 1.0 Universal \| These sources corresponds to the following top-level domains in the dataset: ```python # mapping from domain to top-level domain domain_mapping_dict = { "retsinformationdk": "Legal", "skat": "Legal", "retspraksis": "Legal", "hest": "Social Media", "cc": "Web", "adl": "Wiki & Books", "botxt": "Other", "danavis": "News", "dannet": "dannet", "depbank": "Other", "ep": "Conversation", "ft": "Conversation", "gutenberg": "Wiki & Books", "jvj": "Wiki & Books", "naat": "Conversation", "opensub": "Conversation", "relig": "Wiki & Books", "spont": "Conversation", "synne": "Other", "tv2r": "News", "wiki": "Wiki & Books", "wikibooks": "Wiki & Books", "wikisource": "Wiki & Books", "twfv19": "Social Media", # not present in this version of the dataset } ``` And the following mapping translates between the short form and the long form of the source name ```python # mapping from domain to its long name format longname_mapping_dict = { "retsinformationdk": "retsinformation.dk (Danish legal information)", "skat": "Skat (Danish tax authority)", "retspraksis": "retspraksis (Danish legal information)", "hest": "Hestenettet (Danish debate forum)", "cc": "Common Crawl", "adl": " Archive for Danish Literature", "botxt": "Bornholmsk (Danish dialect)", "danavis": "Danish daily newspapers", "dannet": "DanNet (Danish WordNet)", "depbank": "Danish Dependency Treebank", "ep": "European Parliament", "ft": "Folketinget (Danish Parliament)", "gutenberg": "Gutenberg", "jvj": "Johannes V. Jensen (Danish poet)", "naat": "NAAT", "opensub": "Open Subtitles", "relig": "Religious texts", "spont": "Spontaneous speech", "synne": "Synderjysk (Danish dialect)", "tv2r": "TV 2 Radio (Danish news)", "wiki": "Wikipedia", "wikibooks": "Wikibooks", "wikisource": "Wikisource", "twfv19": "Twitter Folketingsvalget 2019 (Danish election tweets)", # not present in this version of the dataset } ``` ## Additional Information ### Licensing Information If you use the data, you MUST acknowledge it. The license is CC-BY 4.0, Creative Commons with Attribution. ### Citation Information Sample attributions: In a press release: > Modellen er præ-trænet på et datasæt fra The Danish Gigaword Project (https://gigaword.dk), der er udviklet af forskere fra IT-Universitetet i København > The model is pre-trained using the Danish Gigaword Corpus (https://gigaword.dk), developed at the IT University of Copenhagen In academic writing: ``` Derczynski, L., Ciosici, M. R., et al. (2021). The Danish Gigaword Corpus. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021). @inproceedings{dagw, title = {{The Danish Gigaword Corpus}}, author = {Leon Derczynski and Manuel R. Ciosici and Rebekah Baglini and Morten H. Christiansen and Jacob Aarup Dalsgaard and Riccardo Fusaroli and Peter Juel Henrichsen and Rasmus Hvingelby and Andreas Kirkedal and Alex Speed Kjeldsen and Claus Ladefoged and Finn Årup Nielsen and Jens Madsen and Malte Lau Petersen and Jonathan Hvithamar Rystrøm and Daniel Varab}, year = 2021, booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics}, publisher = {NEALT} } ``` In a software product, tool, or service: > Danish Gigaword Corpus: license - homepage > Denne service er lavet med data fra The Danish Gigaword Corpus ### Contributions Dataset created by Derczynski et al. (2021) Derczynski, L., Ciosici, M. R., et al. (2021). The Danish Gigaword Corpus. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021). Thanks to [@HLasse](https://github.com/HLasse) for adding this dataset to the Hugging Face Hub.	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GEM/mlb_data_to_text	GEM	2022-10-24T15:30:20Z	301	1	[ "task_categories:table-to-text", "annotations_creators:none", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:other", "data-to-text", "region:us" ]	[ "table-to-text" ]	2022-03-02T23:29:22Z	--- annotations_creators: - none language_creators: - unknown language: - en license: - other multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: mlb_data_to_text tags: - data-to-text --- # Dataset Card for GEM/mlb_data_to_text ## Dataset Description - Homepage: https://github.com/ratishsp/mlb-data-scripts - Repository: https://github.com/ratishsp/mlb-data-scripts - Paper: https://aclanthology.org/P19-1195 - Leaderboard: N/A - Point of Contact: Ratish Puduppully ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/mlb_data_to_text). ### Dataset Summary The MLB dataset is an English sport-related data-to-text dataset in the baseball domain. The input is a large table with results of a game and the output is a description of the game. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/mlb_data_to_text') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/mlb_data_to_text). #### website [Github](https://github.com/ratishsp/mlb-data-scripts) #### paper [ACL Anthology](https://aclanthology.org/P19-1195) #### authors Ratish Puduppully, Li Dong, Mirella Lapata ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Github](https://github.com/ratishsp/mlb-data-scripts) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Github](https://github.com/ratishsp/mlb-data-scripts) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [ACL Anthology](https://aclanthology.org/P19-1195) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{puduppully-etal-2019-data, title = "Data-to-text Generation with Entity Modeling", author = "Puduppully, Ratish and Dong, Li and Lapata, Mirella", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1195", doi = "10.18653/v1/P19-1195", pages = "2023--2035", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Ratish Puduppully #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> [email protected] #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> other: Other license #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> The dataset can be used to study data-to-text generation. The dataset is in sports domain. It pairs statistics of Major League Baseball (MLB) game with its summary. The summary is in the form of a document containing an average of 540 tokens. Thus it is useful to study long document generation. #### Add. License Info <!-- info: What is the 'other' license of the dataset? --> <!-- scope: periscope --> Restricted to non-commercial research purposes. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Data-to-Text #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> Produce a summary of MLB game from its statistics. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> University of Edinburgh #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Ratish Puduppully, Li Dong, Mirella Lapata ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> ``` features = datasets.Features( { "home_name": datasets.Value("string"), "box_score": [ { "p_l": datasets.Value("string"), "last_name": datasets.Value("string"), "p_h": datasets.Value("string"), "sac": datasets.Value("string"), "p_bb": datasets.Value("string"), "pos": datasets.Value("string"), "ao": datasets.Value("string"), "p_bf": datasets.Value("string"), "cs": datasets.Value("string"), "hbp": datasets.Value("string"), "ab": datasets.Value("string"), "full_name": datasets.Value("string"), "p_w": datasets.Value("string"), "go": datasets.Value("string"), "fldg": datasets.Value("string"), "p_bs": datasets.Value("string"), "avg": datasets.Value("string"), "p_r": datasets.Value("string"), "p_s": datasets.Value("string"), "lob": datasets.Value("string"), "first_name": datasets.Value("string"), "p_sv": datasets.Value("string"), "p_so": datasets.Value("string"), "p_save": datasets.Value("string"), "p_hr": datasets.Value("string"), "po": datasets.Value("string"), "p_ip1": datasets.Value("string"), "p_ip2": datasets.Value("string"), "bb": datasets.Value("string"), "ops": datasets.Value("string"), "p_hld": datasets.Value("string"), "bo": datasets.Value("string"), "p_loss": datasets.Value("string"), "e": datasets.Value("string"), "p_game_score": datasets.Value("string"), "p_win": datasets.Value("string"), "a": datasets.Value("string"), "p_era": datasets.Value("string"), "d": datasets.Value("string"), "p_out": datasets.Value("string"), "h": datasets.Value("string"), "p_er": datasets.Value("string"), "p_np": datasets.Value("string"), "hr": datasets.Value("string"), "r": datasets.Value("string"), "so": datasets.Value("string"), "t": datasets.Value("string"), "rbi": datasets.Value("string"), "team": datasets.Value("string"), "sb": datasets.Value("string"), "slg": datasets.Value("string"), "sf": datasets.Value("string"), "obp": datasets.Value("string"), } ], "home_city": datasets.Value("string"), "vis_name": datasets.Value("string"), "play_by_play": [{ "top": [{ "runs": datasets.Value("string"), "scorers": [ datasets.Value("string") ], "pitcher": datasets.Value("string"), "o": datasets.Value("string"), "b": datasets.Value("string"), "s": datasets.Value("string"), "batter": datasets.Value("string"), "b1": [ datasets.Value("string") ], "b2": [ datasets.Value("string") ], "b3": [ datasets.Value("string") ], "event": datasets.Value("string"), "event2": datasets.Value("string"), "home_team_runs": datasets.Value("string"), "away_team_runs": datasets.Value("string"), "rbi": datasets.Value("string"), "error_runs": datasets.Value("string"), "fielder_error": datasets.Value("string") } ], "bottom": [{ "runs": datasets.Value("string"), "scorers": [ datasets.Value("string") ], "pitcher": datasets.Value("string"), "o": datasets.Value("string"), "b": datasets.Value("string"), "s": datasets.Value("string"), "batter": datasets.Value("string"), "b1": [ datasets.Value("string") ], "b2": [ datasets.Value("string") ], "b3": [ datasets.Value("string") ], "event": datasets.Value("string"), "event2": datasets.Value("string"), "home_team_runs": datasets.Value("string"), "away_team_runs": datasets.Value("string"), "rbi": datasets.Value("string"), "error_runs": datasets.Value("string"), "fielder_error": datasets.Value("string") } ], "inning": datasets.Value("string") } ], "vis_line": { "innings": [{ "inn": datasets.Value("string"), "runs": datasets.Value("string") } ], "result": datasets.Value("string"), "team_runs": datasets.Value("string"), "team_hits": datasets.Value("string"), "team_errors": datasets.Value("string"), "team_name": datasets.Value("string"), "team_city": datasets.Value("string") }, "home_line": { "innings": [{ "inn": datasets.Value("string"), "runs": datasets.Value("string") } ], "result": datasets.Value("string"), "team_runs": datasets.Value("string"), "team_hits": datasets.Value("string"), "team_errors": datasets.Value("string"), "team_name": datasets.Value("string"), "team_city": datasets.Value("string") }, "vis_city": datasets.Value("string"), "day": datasets.Value("string"), "summary": [ datasets.Value("string"), ], "gem_id": datasets.Value("string") } ``` #### Reason for Structure <!-- info: How was the dataset structure determined? --> <!-- scope: microscope --> The high level structure contains the following attributes: home_name, vis_name, home_city, vis_city, summary, summary_eval, day, gem_id, box_score, play_by_play, home_line, vis_line. The attributes home_name, vis_name, home_city, vis_city and day are string values. The attribute "summary" contains the summary in the form of a list of tokens. The attribute "summary_eval" contains the summary in the form of a string of tokens. The difference from "summary" field is that "summary_eval" doesn't contain "NEWPARAGRAPH" delimiters to separate the paragraphs. "summary_eval" field should be used to evaluate model outputs. "summary" field may be used during the training process. box_score contains the box score statistics of the players in the game. It is in the form of a list (of average size 90), with each element describing the statistics of a player. The box score statistics contain 53 attributes. The description of the attributes is given below. The descriptions of most of the attributes is obtained from [mlb.com](https://www.mlb.com/glossary/standard-stats). - r : Runs scored by a player in the game. - rbi Runs Batted In (RBI): action of a batter results in a run scored by other players in the team. - pos Position of the player. - avg Batting Average. It is an indicator of the hits in the players' career. - bb A walk occurs when a pitcher throws four pitches out of the strike zone, none of which are swung at by the hitter. - hr Batter hits the ball in the air over the outfield fence. - p_r Runs given by a pitcher in the game. - p_bb Walks allowed by pitcher in a game. - p_h Hits allowed by pitcher in a game. - p_hr Home runs allowed by pitcher in a game. - p_er Earned Run (ER): An earned run is any run that scores against a pitcher. - p_era Earned Run Average (ERA): Earned run average represents the number of earned runs a pitcher allows per nine innings. - p_np Number of Pitches: A pitcher's total number of pitches is determined by all the pitches he throws in game. - p_ip1 Innings Pitched (IP1): Innings pitched measures the number of innings a pitcher remains in a game. Because there are three outs in an inning, each out recorded represents one-third of an inning pitched. - p_ip2 Innings Pitched (IP2): Innings pitched measures the number of innings a pitcher remains in a game. Because there are three outs in an inning, each out recorded represents one-third of an inning pitched. - p_w A pitcher receives a win when he is the pitcher of record when his team takes the lead for good. - p_l A pitcher receives a loss when a run that is charged to him proves to be the go-ahead run in the game, giving the opposing team a lead it never gives up. - p_so A strikeout occurs when a pitcher throws any combination of three swinging or looking strikes to a hitter. - p_save Save: A save is awarded to the relief pitcher who finishes a game for the winning team. A pitcher cannot receive a save and a win in the same game. - p_sv Saves: The count of saves recorded by a pitcher in his career. - sac A sacrifice fly occurs when a batter hits a fly-ball out to the outfield or foul territory that allows a runner to score. - p_bf Batters faced is simply a count of the number of total plate appearances against a certain pitcher or team. In a perfect game -- with 27 outs -- a pitcher will record 27 batters faced. - cs A caught stealing occurs when a runner attempts to steal but is tagged out before reaching second base, third base or home plate. - hbp A hit-by-pitch occurs when a batter is struck by a pitched ball without swinging at it. He is awarded first base as a result. - ab An official at-bat comes when a batter reaches base via a fielder's choice, hit or an error (not including catcher's interference) or when a batter is put out on a non-sacrifice. - p_bs A blown save occurs when a relief pitcher enters a game in a save situation, but allows the tying run to score. - p_s The count of strikes thrown by a pitcher - lob Left on base can be viewed as both an individual statistic or as a team statistic. In an individual batter's case, it refers to how many men remain on base after that batter makes an out at the plate, as the batter has failed to do his job to score those runners -- or at least put himself in a position to score. In a team's case or in an individual pitcher's case, it refers to the number of men who remain on base at the end of an inning. - po A fielder is credited with a putout when he is the fielder who physically records the act of completing an out -- whether it be by stepping on the base for a forceout, tagging a runner, catching a batted ball, or catching a third strike - ops OPS adds on-base percentage and slugging percentage to get one number that unites the two. It's meant to combine how well a hitter can reach base, with how well he can hit for average and for power. - p_hld A hold occurs when a relief pitcher enters the game in a save situation and maintains his team's lead for the next relief pitcher, while recording at least one out. - p_loss True/False- Indicates losing pitcher - e A fielder is given an error if, in the judgment of the official scorer, he fails to convert an out on a play that an average fielder should have made. - p_win True/False- Indicates winning pitcher - a An assist is awarded to a fielder who touches the ball before a putout is recorded by another fielder. - h A hit occurs when a batter strikes the baseball into fair territory and reaches base without doing so via an error or a fielder's choice. - so A strikeout of a batter - team Team of the player - sb A stolen base occurs when a baserunner advances by taking a base to which he isn't entitled. - slg Slugging percentage represents the total number of bases a player records per at-bat. Unlike on-base percentage, slugging percentage deals only with hits and does not include walks and hit-by-pitches in its equation. - sf A sacrifice fly occurs when a batter hits a fly-ball out to the outfield or foul territory that allows a runner to score. - obp OBP refers to how frequently a batter reaches base per plate appearance. Times on base include hits, walks and hit-by-pitches, but do not include errors, times reached on a fielder's choice or a dropped third strike. The description of attributes in play-by-play is below: - batter Batter in the play. - pitcher Pitcher in play. - b1 Player/s at first base position. - b2 Player/s at second base position. - b3 Player/s at third base position. - scorers Player/s scored in the play. - fielder_error Player committed field error. - event Event of the play such as single, double, home run etc. - event2 Second event of the play such as wild pitch, error etc. - inning Inning of the play. - top/ bottom If home team is batting it is bottom and if away team is batting it is top. - o Count of outs - b Count of balls - s Count of strikes - r Count of runs - rbi Count of runs batted in (rbi) - error_runs Runs due to error - home_team_runs Score of home team - vis_team_runs Score of visiting team `home_line` and `vis_line` contain string value pairs for `team_name`, `team_city`, `team_runs`, `team_hits`, `team_error`, `result`, and a list of runs scored in each inning. #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> There are three splits in the dataset: train, validation and test #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The splits are random. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> This dataset can verify if models are capable of long document generation. The challenges in long document generation conditioned on input tables include ensuring coherent output, staying faithful to the input, ensuring fluent output and avoiding repetition of text. Such aspects can be verified on models trained on this dataset #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> Compared to the existing RotoWire (Wiseman et al. 2017) dataset, MLB summaries are longer (approximately by 50%) and the input records are richer and more structured (with the addition of play-by-play). Moreover, the MLB dataset is five times larger in terms of data size (i.e., pairs of tables and game summaries). #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> Long document generation, coherent ordering of information, faithfulness to the input statistics, fluency in generation and avoiding repetition of text. ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `data points removed` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> Some examples have been removed from training dataset which satisfied the below criteria: 1. The examples in training dataset which overlapped with validation/test. 2. Some examples which described washed out games. #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> The [research paper](https://aclanthology.org/P19-1195) is a good resource ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Automatic evaluation measure can evaluate the factuality, content selection, content ordering and the fluency of the model output. The factuality, content selection and content ordering is measured using an Information Extraction based evaluation approach introduced by Wiseman et al (2017). The fluency is measured using BLEU. #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `Other: Other Metrics` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> Wiseman et al. (2017) define three metrics induced from the outputs of an Information Extraction model which is run on the model/human-written game summaries . Let ŷ be the gold summary and y the model output. • Relation Generation (RG) measures the precision and count of relations extracted from y that also appear in records r. • Content Selection (CS) measures the precision and recall of relations extracted from y that are also extracted from ŷ. • Content Ordering (CO) measures the complement of the normalized Damerau-Levenshtein distance (Brill and Moore, 2000) between the sequences of relations extracted from y and ŷ #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> We have reused the automatic metrics based on Information Extraction evaluation introduced by Wiseman et al (2017). For human evaluation, we conducted studies to evaluate the factuality, coherence, grammaticality and conciseness. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> The most relevant previous results for dataset are in the TACL 2021 paper on [Data-to-text Generation with Macro Planning](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00381/101876/Data-to-text-Generation-with-Macro-Planning) ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> This dataset was curated to complement an existing data-to-text generation dataset (RotoWire by Wiseman et al. 2017) which focuses on long document generation. Compared to RotoWire , MLB summaries are longer (approximately by 50%) and the input records are richer and more structured (with the addition of play-by-play). Moreover, the MLB dataset is five times larger in terms of data size (i.e., pairs of tables and game summaries) #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> The goal is to study automatic generation of long documents in a data-to-text setting. The generated summaries should exhibit coherent ordering of content, be faithful to the input statistics, be fluent and avoid repetition of text. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> no ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Found` #### Where was it found? <!-- info: If found, where from? --> <!-- scope: telescope --> `Single website` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The game summaries are produced by professional writers. #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> The language focuses on the sports domain. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> not validated #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> Game summaries were tokenized using NLTK (Bird et al., 2009) and hyphenated words were separated. Sentences containing quotes were removed as they included opinions and non-factual statements unrelated to the input tables. Sometimes MLB summaries contain a "Game notes" section with incidental information which was also removed. #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> not filtered ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> no #### Justification for Using the Data <!-- info: If not, what is the justification for reusing the data? --> <!-- scope: microscope --> The copyright remains with the original data creators and the usage permission is restricted to non-commercial uses. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> yes/very likely #### Categories of PII <!-- info: What categories of PII are present or suspected in the data? --> <!-- scope: periscope --> `sensitive information`, `generic PII` #### Any PII Identification? <!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? --> <!-- scope: periscope --> no identification ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> unsure ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `research use only` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `research use only` ### Known Technical Limitations	[ -0.4531898498535156, -0.7086378931999207, 0.25788816809654236, 0.18601609766483307, 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NbAiLab/norne	NbAiLab	2022-11-07T12:41:46Z	301	3	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:no", "license:other", "structure-prediction", "arxiv:1911.12146", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - 'no' license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition - part-of-speech tags: - structure-prediction --- # Dataset Card for NorNE: Norwegian Named Entities ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [NorNE](https://github.com/ltgoslo/norne/) - Repository: [Github](https://github.com/ltgoslo/norne/) - Paper: https://arxiv.org/abs/1911.12146 - Leaderboard: - Point of Contact: ### Dataset Summary NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons,organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. ### Supported Tasks and Leaderboards NorNE ads named entity annotations on top of the Norwegian Dependency Treebank. ### Languages Both Norwegian Bokmål (`bokmaal`) and Nynorsk (`nynorsk`) are supported as different configs in this dataset. An extra config for the combined languages is also included (`combined`). See the Annotation section for details on accessing reduced tag sets for the NER feature. ## Dataset Structure Each entry contains text sentences, their language, identifiers, tokens, lemmas, and corresponding NER and POS tag lists. ### Data Instances An example of the `train` split of the `bokmaal` config. ```python {'idx': '000001', 'lang': 'bokmaal', 'lemmas': ['lam', 'og', 'piggvar', 'på', 'bryllupsmeny'], 'ner_tags': [0, 0, 0, 0, 0], 'pos_tags': [0, 9, 0, 5, 0], 'text': 'Lam og piggvar på bryllupsmenyen', 'tokens': ['Lam', 'og', 'piggvar', 'på', 'bryllupsmenyen']} ``` ### Data Fields Each entry is annotated with the next fields: - `idx` (`int`), text (sentence) identifier from the NorNE dataset - `lang` (`str`), language variety, either `bokmaal`, `nynorsk` or `combined` - `text` (`str`), plain text - `tokens` (`List[str]`), list of tokens extracted from `text` - `lemmas` (`List[str]`), list of lemmas extracted from `tokens` - `ner_tags` (`List[int]`), list of numeric NER tags for each token in `tokens` - `pos_tags` (`List[int]`), list of numeric PoS tags for each token in `tokens` An example DataFrame obtained from the dataset: <table class="dataframe" border="1"> <thead> <tr style="text-align: right;"> <th></th> <th>idx</th> <th>lang</th> <th>text</th> <th>tokens</th> <th>lemmas</th> <th>ner_tags</th> <th>pos_tags</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>000001</td> <td>bokmaal</td> <td>Lam og piggvar på bryllupsmenyen</td> <td>[Lam, og, piggvar, på, bryllupsmenyen]</td> <td>[lam, og, piggvar, på, bryllupsmeny]</td> <td>[0, 0, 0, 0, 0]</td> <td>[0, 9, 0, 5, 0]</td> </tr> <tr> <th>1</th> <td>000002</td> <td>bokmaal</td> <td>Kamskjell, piggvar og lammefilet sto på menyen...</td> <td>[Kamskjell, ,, piggvar, og, lammefilet, sto, p...</td> <td>[kamskjell, $,, piggvar, og, lammefilet, stå, ...</td> <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td> <td>[0, 1, 0, 9, 0, 15, 2, 0, 2, 8, 6, 0, 1]</td> </tr> <tr> <th>2</th> <td>000003</td> <td>bokmaal</td> <td>Og til dessert: Parfait à la Mette-Marit.</td> <td>[Og, til, dessert, :, Parfait, à, la, Mette-Ma...</td> <td>[og, til, dessert, $:, Parfait, à, la, Mette-M...</td> <td>[0, 0, 0, 0, 7, 8, 8, 8, 0]</td> <td>[9, 2, 0, 1, 10, 12, 12, 10, 1]</td> </tr> </tbody> </table> ### Data Splits There are three splits: `train`, `validation` and `test`. \| Config \| Split \| Total \| \| :---------\|-------------:\|-------:\| \| `bokmaal` \| `train` \| 15696 \| \| `bokmaal` \| `validation` \| 2410 \| \| `bokmaal` \| `test` \| 1939 \| \| `nynorsk` \| `train` \| 14174 \| \| `nynorsk` \| `validation` \| 1890 \| \| `nynorsk` \| `test` \| 1511 \| \| `combined`\| `test` \| 29870 \| \| `combined`\| `validation` \| 4300 \| \| `combined`\| `test` \| 3450 \| ## Dataset Creation ### Curation Rationale 1. A _name_ in this context is close to [Saul Kripke's definition of a name](https://en.wikipedia.org/wiki/Saul_Kripke#Naming_and_Necessity), in that a name has a unique reference and its meaning is constant (there are exceptions in the annotations, e.g. "Regjeringen" (en. "Government")). 2. It is the usage of a name that determines the entity type, not the default/literal sense of the name, 3. If there is an ambiguity in the type/sense of a name, then the the default/literal sense of the name is chosen (following [Markert and Nissim, 2002](http://www.lrec-conf.org/proceedings/lrec2002/pdf/11.pdf)). For more details, see the "Annotation Guidelines.pdf" distributed with the corpus. ### Source Data Data was collected using blogs and newspapers in Norwegian, as well as parliament speeches and governamental reports. #### Initial Data Collection and Normalization The texts in the Norwegian Dependency Treebank (NDT) are manually annotated with morphological features, syntactic functions and hierarchical structure. The formalism used for the syntactic annotation is dependency grammar. The treebanks consists of two parts, one part in Norwegian Bokmål (`nob`) and one part in Norwegian Nynorsk (`nno`). Both parts contain around 300.000 tokens, and are a mix of different non-fictional genres. See the [NDT webpage](https://www.nb.no/sprakbanken/show?serial=sbr-10) for more details. ### Annotations The following types of entities are annotated: - Person (`PER`): Real or fictional characters and animals - Organization (`ORG`): Any collection of people, such as firms, institutions, organizations, music groups, sports teams, unions, political parties etc. - Location (`LOC`): Geographical places, buildings and facilities - Geo-political entity (`GPE`): Geographical regions defined by political and/or social groups. A GPE entity subsumes and does not distinguish between a nation, its region, its government, or its people - Product (`PROD`): Artificially produced entities are regarded products. This may include more abstract entities, such as speeches, radio shows, programming languages, contracts, laws and ideas. - Event (`EVT`): Festivals, cultural events, sports events, weather phenomena, wars, etc. Events are bounded in time and space. - Derived (`DRV`): Words (and phrases?) that are dervied from a name, but not a name in themselves. They typically contain a full name and are capitalized, but are not proper nouns. Examples (fictive) are "Brann-treneren" ("the Brann coach") or "Oslo-mannen" ("the man from Oslo"). - Miscellaneous (`MISC`): Names that do not belong in the other categories. Examples are animals species and names of medical conditions. Entities that are manufactured or produced are of type Products, whereas thing naturally or spontaneously occurring are of type Miscellaneous. Furthermore, all `GPE` entities are additionally sub-categorized as being either `ORG` or `LOC`, with the two annotation levels separated by an underscore: - `GPE_LOC`: Geo-political entity, with a locative sense (e.g. "John lives in _Spain_") - `GPE_ORG`: Geo-political entity, with an organisation sense (e.g. "_Spain_ declined to meet with Belgium") The two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. This means that the following sets of entity types can be derived: - 7 types, deleting `_GPE`: `ORG`, `LOC`, `PER`, `PROD`, `EVT`, `DRV`, `MISC` - 8 types, deleting `LOC_` and `ORG_`: `ORG`, `LOC`, `GPE`, `PER`, `PROD`, `EVT`, `DRV`, `MISC` - 9 types, keeping all types: `ORG`, `LOC`, `GPE_LOC`, `GPE_ORG`, `PER`, `PROD`, `EVT`, `DRV`, `MISC` The class distribution is as follows, broken down across the data splits of the UD version of NDT, and sorted by total counts (i.e. the number of examples, not tokens within the spans of the annotatons): \| Type \| Train \| Dev \| Test \| Total \| \| :--------\|-------:\|-------:\|-------:\|-------:\| \| `PER` \| 4033 \| 607 \| 560 \| 5200 \| \| `ORG` \| 2828 \| 400 \| 283 \| 3511 \| \| `GPE_LOC`\| 2132 \| 258 \| 257 \| 2647 \| \| `PROD` \| 671 \| 162 \| 71 \| 904 \| \| `LOC` \| 613 \| 109 \| 103 \| 825 \| \| `GPE_ORG`\| 388 \| 55 \| 50 \| 493 \| \| `DRV` \| 519 \| 77 \| 48 \| 644 \| \| `EVT` \| 131 \| 9 \| 5 \| 145 \| \| `MISC` \| 8 \| 0 \| 0 \| 0 \| To access these reduce versions of the dataset, you can use the configs `bokmaal-7`, `nynorsk-7`, `combined-7` for the NER tag set with 7 tags ( `ORG`, `LOC`, `PER`, `PROD`, `EVT`, `DRV`, `MISC`), and `bokmaal-8`, `nynorsk-8`, `combined-8` for the NER tag set with 8 tags (`LOC_` and `ORG_`: `ORG`, `LOC`, `GPE`, `PER`, `PROD`, `EVT`, `DRV`, `MISC`). By default, the full set (9 tags) will be used. ## Additional Information ### Dataset Curators NorNE was created as a collaboration between [Schibsted Media Group](https://schibsted.com/), [Språkbanken](https://www.nb.no/forskning/sprakbanken/) at the [National Library of Norway](https://www.nb.no) and the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) at the University of Oslo. NorNE was added to Huggingface Datasets by the AI-Lab at the National Library of Norway. ### Licensing Information The NorNE corpus is published under the same [license](https://github.com/ltgoslo/norne/blob/master/LICENSE_NDT.txt) as the Norwegian Dependency Treebank ### Citation Information This dataset is described in the paper _NorNE: Annotating Named Entities for Norwegian_ by Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal, accepted for LREC 2020 and available as pre-print here: https://arxiv.org/abs/1911.12146.	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PlanTL-GOB-ES/pharmaconer	PlanTL-GOB-ES	2022-11-18T12:06:36Z	301	4	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "biomedical", "clinical", "spanish", "region:us" ]	[ "token-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language: - es tags: - biomedical - clinical - spanish multilinguality: - monolingual task_categories: - token-classification task_ids: - named-entity-recognition license: - cc-by-4.0 --- # PharmaCoNER ## Dataset Description Manually classified collection of Spanish clinical case studies. - Homepage: [zenodo](https://zenodo.org/record/4270158) - Paper: [PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track](https://aclanthology.org/D19-5701/) - Point of Contact: [email protected] ### Dataset Summary Manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from [SciELO](https://scielo.org/). The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each. In terms of training examples, this translates to a total of 8129, 3787 and 3952 annotated sentences in each set. The original dataset is distributed in [Brat](https://brat.nlplab.org/standoff.html) format. The annotation of the entire set of entity mentions was carried out by domain experts. It includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR. This dataset was designed for the PharmaCoNER task, sponsored by [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx). For further information, please visit [the official website](https://temu.bsc.es/pharmaconer/). ### Supported Tasks Named Entity Recognition (NER) ### Languages - Spanish (es) ### Directory Structure * README.md * pharmaconer.py * dev-set_1.1.conll * test-set_1.1.conll * train-set_1.1.conll ## Dataset Structure ### Data Instances Three four-column files, one for each split. ### Data Fields Every file has four columns: * 1st column: Word form or punctuation symbol * 2nd column: Original BRAT file name * 3rd column: Spans * 4th column: IOB tag #### Example <pre> La S0004-06142006000900008-1 123_125 O paciente S0004-06142006000900008-1 126_134 O tenía S0004-06142006000900008-1 135_140 O antecedentes S0004-06142006000900008-1 141_153 O de S0004-06142006000900008-1 154_156 O hipotiroidismo S0004-06142006000900008-1 157_171 O , S0004-06142006000900008-1 171_172 O hipertensión S0004-06142006000900008-1 173_185 O arterial S0004-06142006000900008-1 186_194 O en S0004-06142006000900008-1 195_197 O tratamiento S0004-06142006000900008-1 198_209 O habitual S0004-06142006000900008-1 210_218 O con S0004-06142006000900008-1 219-222 O atenolol S0004-06142006000900008-1 223_231 B-NORMALIZABLES y S0004-06142006000900008-1 232_233 O enalapril S0004-06142006000900008-1 234_243 B-NORMALIZABLES </pre> ### Data Splits \| Split \| Size \| \| ------------- \| ------------- \| \| `train` \| 8,129 \| \| `dev` \| 3,787 \| \| `test` \| 3,952 \| ## Dataset Creation ### Curation Rationale For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines. ### Source Data #### Initial Data Collection and Normalization Manually classified collection of clinical case report sections. The clinical cases were not restricted to a single medical discipline, covering a variety of medical disciplines, including oncology, urology, cardiology, pneumology or infectious diseases. This is key to cover a diverse set of chemicals and drugs. #### Who are the source language producers? Humans, there is no machine generated data. ### Annotations #### Annotation process The annotation process of the PharmaCoNER corpus was inspired by previous annotation schemes and corpora used for the BioCreative CHEMDNER and GPRO tracks, translating the guidelines used for these tracks into Spanish and adapting them to the characteristics and needs of clinically oriented documents by modifying the annotation criteria and rules to cover medical information needs. This adaptation was carried out in collaboration with practicing physicians and medicinal chemistry experts. The adaptation, translation and refinement of the guidelines was done on a sample set of the SPACCC corpus and linked to an iterative process of annotation consistency analysis through interannotator agreement (IAA) studies until a high annotation quality in terms of IAA was reached. #### Who are the annotators? Practicing physicians and medicinal chemistry experts. ### Personal and Sensitive Information No personal or sensitive information included. ## Considerations for Using the Data ### Social Impact of Dataset This corpus contributes to the development of medical language models in Spanish. ### Discussion of Biases [N/A] ## Additional Information ### Dataset Curators Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]). For further information, send an email to ([email protected]). This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx). ### Licensing information This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License. Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) ### Citation Information ```bibtex @inproceedings{, title = "PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track", author = "Gonzalez-Agirre, Aitor and Marimon, Montserrat and Intxaurrondo, Ander and Rabal, Obdulia and Villegas, Marta and Krallinger, Martin", booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D19-5701", doi = "10.18653/v1/D19-5701", pages = "1--10", } ``` ### Contributions [N/A]	[ -0.28838151693344116, -0.4355115294456482, 0.5339738130569458, 0.32134267687797546, -0.20297080278396606, -0.04697834327816963, -0.2573932409286499, -0.4757833778858185, 0.607541024684906, 0.5619745254516602, -0.3941569924354553, -0.9944045543670654, -0.8455274701118469, 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jonathan-roberts1/SIRI-WHU	jonathan-roberts1	2023-03-31T17:18:08Z	301	1	[ "task_categories:image-classification", "task_categories:zero-shot-image-classification", "license:other", "region:us" ]	[ "image-classification", "zero-shot-image-classification" ]	2023-01-20T15:46:58Z	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': agriculture '1': commercial '2': harbor '3': idle_land '4': industrial '5': meadow '6': overpass '7': park '8': pond '9': residential '10': river '11': water splits: - name: train num_bytes: 158215614.4 num_examples: 2400 download_size: 147702566 dataset_size: 158215614.4 license: other task_categories: - image-classification - zero-shot-image-classification --- # Dataset Card for "SIRI-WHU" ## Dataset Description - Paper [Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/36/4358825/07329997.pdf) - Paper [The Fisher kernel coding framework for high spatial resolution scene classification](https://www.mdpi.com/2072-4292/8/2/157/pdf) - Paper [Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/8859/7473942/07466064.pdf) ### Licensing Information CC BY-NC-ND ## Citation Information [Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/36/4358825/07329997.pdf) [The Fisher kernel coding framework for high spatial resolution scene classification](https://www.mdpi.com/2072-4292/8/2/157/pdf) [Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/8859/7473942/07466064.pdf) ``` @article{zhao2015dirichlet, title={Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery}, author={Zhao, Bei and Zhong, Yanfei and Xia, Gui-Song and Zhang, Liangpei}, journal={IEEE Transactions on Geoscience and Remote Sensing}, volume={54}, number={4}, pages={2108--2123}, year={2015}, publisher={IEEE} } @article{zhao2016fisher, title={The Fisher kernel coding framework for high spatial resolution scene classification}, author={Zhao, Bei and Zhong, Yanfei and Zhang, Liangpei and Huang, Bo}, journal={Remote Sensing}, volume={8}, number={2}, pages={157}, year={2016}, publisher={MDPI} } @article{zhu2016bag, title={Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery}, author={Zhu, Qiqi and Zhong, Yanfei and Zhao, Bei and Xia, Gui-Song and Zhang, Liangpei}, journal={IEEE Geoscience and Remote Sensing Letters}, volume={13}, number={6}, pages={747--751}, year={2016}, publisher={IEEE} } ```	[ -0.6619800329208374, -0.449815958738327, 0.24436214566230774, 0.059381674975156784, -0.08879349380731583, 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open-llm-leaderboard/details_huggingface__llama-13b	open-llm-leaderboard	2023-09-23T00:12:09Z	301	0	[ "region:us" ]	null	2023-08-18T11:15:04Z	--- pretty_name: Evaluation run of huggingface/llama-13b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [huggingface/llama-13b](https://huggingface.co/huggingface/llama-13b) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_huggingface__llama-13b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T00:11:57.766802](https://huggingface.co/datasets/open-llm-leaderboard/details_huggingface__llama-13b/blob/main/results_2023-09-23T00-11-57.766802.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\": 0.0019924496644295304,\n\ \ \"em_stderr\": 0.000456667646266702,\n \"f1\": 0.056602348993288636,\n\ \ \"f1_stderr\": 0.0013004668300984712,\n \"acc\": 0.4191229752993855,\n\ \ \"acc_stderr\": 0.009626252314482865\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0019924496644295304,\n \"em_stderr\": 0.000456667646266702,\n\ \ \"f1\": 0.056602348993288636,\n \"f1_stderr\": 0.0013004668300984712\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0758150113722517,\n \ \ \"acc_stderr\": 0.007291205723162579\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803152\n\ \ }\n}\n```" repo_url: https://huggingface.co/huggingface/llama-13b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|arc:challenge\|25_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-20T10:00:13.254246.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T00_11_57.766802 path: - '/details_harness\|drop\|3_2023-09-23T00-11-57.766802.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-23T00-11-57.766802.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T00_11_57.766802 path: - '/details_harness\|gsm8k\|5_2023-09-23T00-11-57.766802.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-23T00-11-57.766802.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hellaswag\|10_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-20T10:00:13.254246.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-20T10:00:13.254246.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_20T10_00_13.254246 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-20T10:00:13.254246.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-20T10:00:13.254246.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T00_11_57.766802 path: - '/details_harness\|winogrande\|5_2023-09-23T00-11-57.766802.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-23T00-11-57.766802.parquet' - config_name: results data_files: - split: 2023_07_20T10_00_13.254246 path: - results_2023-07-20T10:00:13.254246.parquet - split: 2023_09_23T00_11_57.766802 path: - results_2023-09-23T00-11-57.766802.parquet - split: latest path: - results_2023-09-23T00-11-57.766802.parquet --- # Dataset Card for Evaluation run of huggingface/llama-13b ## Dataset Description - Homepage: - Repository: https://huggingface.co/huggingface/llama-13b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [huggingface/llama-13b](https://huggingface.co/huggingface/llama-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_huggingface__llama-13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T00:11:57.766802](https://huggingface.co/datasets/open-llm-leaderboard/details_huggingface__llama-13b/blob/main/results_2023-09-23T00-11-57.766802.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": 0.0019924496644295304, "em_stderr": 0.000456667646266702, "f1": 0.056602348993288636, "f1_stderr": 0.0013004668300984712, "acc": 0.4191229752993855, "acc_stderr": 0.009626252314482865 }, "harness\|drop\|3": { "em": 0.0019924496644295304, "em_stderr": 0.000456667646266702, "f1": 0.056602348993288636, "f1_stderr": 0.0013004668300984712 }, "harness\|gsm8k\|5": { "acc": 0.0758150113722517, "acc_stderr": 0.007291205723162579 }, "harness\|winogrande\|5": { "acc": 0.7624309392265194, "acc_stderr": 0.011961298905803152 } } ``` ### 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]	[ -0.42835164070129395, -0.6994760632514954, 0.2146940529346466, 0.4043591022491455, -0.2042372077703476, 0.19471320509910583, -0.3285122215747833, -0.3005289137363434, 0.5584481954574585, 0.49025145173072815, -0.749671220779419, -0.9227447509765625, -0.7712191343307495, 0.24990339577198029, -0.1820240616798401, 1.1748121976852417, -0.2757478356361389, -0.3527480363845825, -0.027002865448594093, -0.3217371702194214, -0.2591112554073334, -0.3936852514743805, -0.4054011106491089, 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open-llm-leaderboard/details_concedo__Pythia-70M-ChatSalad	open-llm-leaderboard	2023-09-22T19:59:24Z	301	0	[ "region:us" ]	null	2023-08-18T11:52:15Z	--- pretty_name: Evaluation run of concedo/Pythia-70M-ChatSalad dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [concedo/Pythia-70M-ChatSalad](https://huggingface.co/concedo/Pythia-70M-ChatSalad)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_concedo__Pythia-70M-ChatSalad\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T19:59:13.355253](https://huggingface.co/datasets/open-llm-leaderboard/details_concedo__Pythia-70M-ChatSalad/blob/main/results_2023-09-22T19-59-13.355253.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\": 0.0014681208053691276,\n\ \ \"em_stderr\": 0.00039210421902982634,\n \"f1\": 0.008363045302013424,\n\ \ \"f1_stderr\": 0.0006175853648384896,\n \"acc\": 0.26203630623520124,\n\ \ \"acc_stderr\": 0.0070180948326975644\n },\n \"harness\|drop\|3\":\ \ {\n \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.00039210421902982634,\n\ \ \"f1\": 0.008363045302013424,\n \"f1_stderr\": 0.0006175853648384896\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.5240726124704025,\n\ \ \"acc_stderr\": 0.014036189665395129\n }\n}\n```" repo_url: https://huggingface.co/concedo/Pythia-70M-ChatSalad leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:36:47.045814.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_22T19_59_13.355253 path: - '/details_harness\|drop\|3_2023-09-22T19-59-13.355253.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-22T19-59-13.355253.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T19_59_13.355253 path: - '/details_harness\|gsm8k\|5_2023-09-22T19-59-13.355253.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-22T19-59-13.355253.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hellaswag\|10_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:36:47.045814.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:36:47.045814.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_36_47.045814 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:36:47.045814.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:36:47.045814.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T19_59_13.355253 path: - '/details_harness\|winogrande\|5_2023-09-22T19-59-13.355253.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-22T19-59-13.355253.parquet' - config_name: results data_files: - split: 2023_07_19T13_36_47.045814 path: - results_2023-07-19T13:36:47.045814.parquet - split: 2023_09_22T19_59_13.355253 path: - results_2023-09-22T19-59-13.355253.parquet - split: latest path: - results_2023-09-22T19-59-13.355253.parquet --- # Dataset Card for Evaluation run of concedo/Pythia-70M-ChatSalad ## Dataset Description - Homepage: - Repository: https://huggingface.co/concedo/Pythia-70M-ChatSalad - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [concedo/Pythia-70M-ChatSalad](https://huggingface.co/concedo/Pythia-70M-ChatSalad) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_concedo__Pythia-70M-ChatSalad", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T19:59:13.355253](https://huggingface.co/datasets/open-llm-leaderboard/details_concedo__Pythia-70M-ChatSalad/blob/main/results_2023-09-22T19-59-13.355253.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": 0.0014681208053691276, "em_stderr": 0.00039210421902982634, "f1": 0.008363045302013424, "f1_stderr": 0.0006175853648384896, "acc": 0.26203630623520124, "acc_stderr": 0.0070180948326975644 }, "harness\|drop\|3": { "em": 0.0014681208053691276, "em_stderr": 0.00039210421902982634, "f1": 0.008363045302013424, "f1_stderr": 0.0006175853648384896 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.5240726124704025, "acc_stderr": 0.014036189665395129 } } ``` ### 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]	[ -0.33726954460144043, -0.6390715837478638, 0.1337502896785736, 0.2089732438325882, -0.22593790292739868, 0.13199156522750854, -0.47754231095314026, -0.18417805433273315, 0.34009918570518494, 0.5286762118339539, -0.6635218858718872, -0.9578827023506165, -0.689959704875946, 0.1435369849205017, -0.14743070304393768, 1.1294201612472534, -0.2642286717891693, -0.13295230269432068, 0.022332195192575455, -0.248782217502594, -0.33090999722480774, -0.46236929297447205, -0.4833258092403412, 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vwxyzjn/cai-conversation-prod-h4-harmless	vwxyzjn	2023-12-01T14:30:02Z	301	0	[ "region:us" ]	null	2023-11-30T19:30:03Z	--- configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* dataset_info: features: - name: index dtype: int64 - name: prompt dtype: string - name: init_prompt struct: - name: content dtype: string - name: role dtype: string - name: init_response struct: - name: content dtype: string - name: role dtype: string - name: critic_prompt struct: - name: content dtype: string - name: role dtype: string - name: critic_response struct: - name: content dtype: string - name: role dtype: string - name: revision_prompt struct: - name: content dtype: string - name: role dtype: string - name: revision_response struct: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 80509011.39550994 num_examples: 21289 - name: test_sft num_bytes: 4409523.340505145 num_examples: 1156 - name: train_prefs num_bytes: 80509011.39550994 num_examples: 21289 - name: test_prefs num_bytes: 4413337.807062675 num_examples: 1157 download_size: 52332286 dataset_size: 169840883.93858773 --- # Dataset Card for "cai-conversation-prod-h4-harmless" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.44408363103866577, -0.6797594428062439, 0.01756368763744831, 0.30692991614341736, -0.25752219557762146, -0.09792253375053406, 0.3001885712146759, -0.31471553444862366, 0.8000630736351013, 0.5422805547714233, -0.608212947845459, -0.8883571624755859, -0.5667015314102173, 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id_panl_bppt	null	2023-01-25T14:32:43Z	300	1	[ "task_categories:translation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:id", "license:unknown", "region:us" ]	[ "translation" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - id license: - unknown multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: IdPanlBppt dataset_info: features: - name: id dtype: string - name: translation dtype: translation: languages: - en - id - name: topic dtype: class_label: names: '0': Economy '1': International '2': Science '3': Sport config_name: id_panl_bppt splits: - name: train num_bytes: 7455924 num_examples: 24021 download_size: 2366973 dataset_size: 7455924 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [PANL BPPT](http://digilib.bppt.go.id/sampul/p92-budiono.pdf) - Repository: [PANL BPPT Repository](https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/BPPTIndToEngCorpusHalfM.zip) - Paper: [Resource Report: Building Parallel Text Corpora for Multi-Domain Translation System](http://digilib.bppt.go.id/sampul/p92-budiono.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international, Science and Technology and Sport). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure [More Information Needed] ### Data Instances An example of the dataset: ``` { 'id': '0', 'topic': 0, 'translation': { 'en': 'Minister of Finance Sri Mulyani Indrawati said that a sharp correction of the composite inde x by up to 4 pct in Wedenesday?s trading was a mere temporary effect of regional factors like decline in plantation commodity prices and the financial crisis in Thailand.', 'id': 'Menteri Keuangan Sri Mulyani mengatakan koreksi tajam pada Indeks Harga Saham Gabungan IHSG hingga sekitar 4 persen dalam perdagangan Rabu 10/1 hanya efek sesaat dari faktor-faktor regional seperti penurunan harga komoditi perkebunan dan krisis finansial di Thailand.' } } ``` ### Data Fields - `id`: id of the sample - `translation`: the parallel sentence english-indonesian - `topic`: the topic of the sentence. It could be one of the following: - Economic - International - Science and Technology - Sport ### Data Splits The dataset is splitted in to train, validation and test sets. ## 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 ``` @inproceedings{id_panl_bppt, author = {PAN Localization - BPPT}, title = {Parallel Text Corpora, English Indonesian}, year = {2009}, url = {http://digilib.bppt.go.id/sampul/p92-budiono.pdf}, } ``` ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.	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journalists_questions	null	2023-01-25T14:33:26Z	300	0	[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ar", "license:unknown", "question-identification", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - crowdsourced language_creators: - other language: - ar license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: JournalistsQuestions tags: - question-identification dataset_info: features: - name: tweet_id dtype: string - name: label dtype: class_label: names: '0': 'no' '1': 'yes' - name: label_confidence dtype: float32 config_name: plain_text splits: - name: train num_bytes: 342296 num_examples: 10077 download_size: 271039 dataset_size: 342296 --- # Dataset Card for journalists_questions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://qufaculty.qu.edu.qa/telsayed/datasets/ - Repository: [Needs More Information] - Paper: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/download/13221/12856 - Leaderboard: [Needs More Information] - Point of Contact: [Maram Hasanain] [email protected] ### Dataset Summary The journalists_questions dataset supports question identification over Arabic tweets of journalists. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Arabic ## Dataset Structure ### Data Instances Our dataset supports question identification task. It includes 10K Arabic tweets crawled from journalists accounts. Tweets were labelled by crowdsourcing. Each tweet is associated with one label: question tweet or not. A question tweet is a tweet that has at least one interrogative question. Each label is associated with a number that represents the confidence in the label, given that each tweet was labelled by 3 annotators and an aggregation method was followed to choose the final label. Below is an example: { 'tweet_id': '493235142128074753', 'label': 'yes', 'label_confidence':0.6359 } ### Data Fields tweet_id: the Twitter assigned ID for the tweet object. label: annotation of the tweet by whether it is a question or not label_confidence: confidence score for the label given annotations of multiple annotators per tweet ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale The dataset includes tweet IDs only due to Twitter content re-distribution policy. It was created and shared for research purposes for parties interested in understanding questions expecting answers by Arab journalists on Twitter. ### Source Data #### Initial Data Collection and Normalization To construct our dataset of question tweets posted by journalists, we first acquire a list of Twitter accounts of 389 Arab journalists. We use the Twitter API to crawl their available tweets, keeping only those that are identified by Twitter to be both Arabic, and not retweets (as these would contain content that was not originally authored by journalists). We apply a rule-based question filter to this dataset of 465,599 tweets, extracting 49,119 (10.6%) potential question tweets from 363 (93.3%) Arab journalists. #### 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 Thanks to [@MaramHasanain](https://github.com/MaramHasanain) for adding this dataset.	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time_dial	null	2022-11-03T16:07:53Z	300	4	[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0", "dialog-act-classification", "arxiv:2106.04571", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: 'TimeDial: Temporal Commonsense Reasoning in Dialog' size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification paperswithcode_id: timedial tags: - dialog-act-classification dataset_info: features: - name: id dtype: int32 - name: conversation sequence: string - name: correct1 dtype: string - name: correct2 dtype: string - name: incorrect1 dtype: string - name: incorrect1_rule dtype: string - name: incorrect2 dtype: string - name: incorrect2_rule dtype: string splits: - name: test num_bytes: 1449879 num_examples: 1446 download_size: 1613806 dataset_size: 1449879 --- # Dataset Card for TimeDial: Temporal Commonsense Reasoning in Dialog ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [TimeDial](https://github.com/google-research-datasets/timedial) - Paper: [TimeDial: Temporal Commonsense Reasoning in Dialog](https://arxiv.org/abs/2106.04571) - Point of Contact: [Please create an issue in the official repository](https://github.com/google-research-datasets/timedial) ### Dataset Summary TimeDial presents a crowdsourced English challenge set, for temporal commonsense reasoning, formulated as a multiple choice cloze task with around 1.5k carefully curated dialogs. The dataset is derived from the DailyDialog ([Li et al., 2017](https://www.aclweb.org/anthology/I17-1099/)), which is a multi-turn dialog corpus. In order to establish strong baselines and provide information on future model development, the authors conducted extensive experiments with state-of-the-art LMs. While humans can easily answer these questions (97.8\%), the best T5 model variant struggles on this challenge set (73\%). Moreover, our qualitative error analyses show that the models often rely on shallow, spurious features (particularly text matching), instead of truly doing reasoning over the context. Detailed experiments and analyses can be found in their [paper](https://arxiv.org/pdf/2106.04571.pdf). ### Supported Tasks and Leaderboards To be updated soon. ### Languages The dataset is in English only. ## Dataset Structure ### Data Instances ``` { "id": 1, "conversation": [ "A: We need to take the accounts system offline to carry out the upgrade . But don't worry , it won't cause too much inconvenience . We're going to do it over the weekend .", "B: How long will the system be down for ?", "A: We'll be taking everything offline in about two hours ' time . It'll be down for a minimum of twelve hours . If everything goes according to plan , it should be up again by 6 pm on Saturday .", "B: That's fine . We've allowed <MASK> to be on the safe side ." ], "correct1": "forty-eight hours", "correct2": "50 hours ", "incorrect1": "two hours ", "incorrect1_rule": "Rule 1", "incorrect2": "12 days ", "incorrect2_rule": "Rule 2" } ``` ### Data Fields - "id": Unique identifier, as a integer - "conversation": Dialog context with <MASK> span, as a string - "correct1": Original <MASK> span, as a string - "correct2": Additional correct option provided by annotators, as a string - "incorrect1": Incorrect option #1 provided by annotators, as a string - "incorrect1_rule": One of phrase matching ("Rule 1"), numeral matching ("Rule 2"), or open ended ("Rule 3"), as a string - "incorrect2": Incorrect option #2 provided by annotators, as a string - "incorrect2_rule": One of phrase matching ("Rule 1"), numeral matching ("Rule 2"), or open ended ("Rule 3"), as a string ### Data Splits TimeDial dataset consists only of a test set of 1,104 dialog instances with 2 correct and 2 incorrect options with the following statistics: \| \| Avg. \| \|-----\|-----\| \|Turns per Dialog \| 11.7 \| \|Words per Turn \| 16.5 \| \|Time Spans per Dialog \| 3 \| ## Dataset Creation ### Curation Rationale Although previous works have studied temporal reasoning in natural language, they have either focused on specific time-related concepts in isolation, such as temporal ordering and relation extraction, and/or dealt with limited context, such as single-sentence-based question answering and natural language inference. In this work, they make the first systematic study of temporal commonsense reasoning in a multi-turn dialog setting. The task involves complex reasoning that requires operations like comparison and arithmetic reasoning over temporal expressions and the need for commonsense and world knowledge. ### Source Data #### Initial Data Collection and Normalization The TIMEDIAL dataset is derived from DailyDialog data (Li et al., 2017), which is a multi-turn dialog corpus containing over 13K English dialogs. Dialogs in this dataset consist of turn-taking between two people on topics over 10 broad categories, ranging from daily lives to financial topics. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The data collection process involves two steps: (1) identifying dialogs that are rich in temporal expressions, and (2) asking human annotators to provide correct and incorrect options for cloze instances derived from these dialogs. More details about the two steps: 1) Temporal expression identification: Here, they select dialogs that are rich with temporal information, in order to focus on complex temporal reasoning that arises in natural dialogs. Temporal expressions are automatically identified with SU-Time, an off-the-shelf temporal expression detector. They keep only the dialogs with more than 3 temporal expressions and at least one expression that contains numerals like “two weeks” (as opposed to non-numeric spans, like “summer”, “right now”, and “later”). In their initial experiment, they observe that language models can often correctly predict these non-numerical temporal phrases. 2) Human annotated options: Next, they make spans in the dialogs. For a dialog, they mask out each temporal expression that contains numerals, each resulting in a cloze question that is then sent for human annotation. This resulted in 1,526 instances for annotation. For each masked span in each dialog, they obtain human annotation to derive a fixed set of correct and incorrect options given the context. Concretely, given a masked dialog and a seed correct answer (i.e., the original text) for the masked span, the annotators were asked to (1) come up with an alternative correct answer that makes sense in the dialog adhering to commonsense, and (2) formulate two incorrect answers that have no possibility of making sense in the dialog context. They highlight all time expressions in the context to make it easier for annotators to select reasonable time expressions. #### Who are the annotators? They are English linguists. ### 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 Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information TimeDial dataset is licensed under CC BY-NC-SA 4.0. ### Citation Information ``` @inproceedings{qin-etal-2021-timedial, title = "{TimeDial: Temporal Commonsense Reasoning in Dialog}", author = "Qin, Lianhui and Gupta, Aditya and Upadhyay, Shyam and He, Luheng and Choi, Yejin and Faruqui, Manaal", booktitle = "Proc. of ACL", year = "2021" } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.	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blinoff/medical_qa_ru_data	blinoff	2022-07-02T06:24:13Z	300	6	[ "task_categories:question-answering", "task_ids:closed-domain-qa", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ru", "license:unknown", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	--- annotations_creators: [] language_creators: [] language: - ru license: - unknown multilinguality: - monolingual pretty_name: Medical Q&A Russian Data size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa --- ### Dataset Summary This dataset contains 190,335 Russian Q&A posts from a medical related forum. ### Dataset Fields * date: date and time of the asked question, like '26 Октября 2018, 08:30' * categ: question category * theme: question topic * desc: question text * ans: question answers separated with ';\n' * spec10: if present, one of 10 medical specializations	[ -0.4382667541503906, 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dream-textures/textures-color-1k	dream-textures	2023-01-13T17:54:04Z	300	6	[ "task_categories:text-to-image", "size_categories:1K<n<10K", "language:en", "license:cc0-1.0", "region:us" ]	[ "text-to-image" ]	2023-01-13T17:27:40Z	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 60933571.47 num_examples: 1426 download_size: 58351352 dataset_size: 60933571.47 license: cc0-1.0 task_categories: - text-to-image language: - en size_categories: - 1K<n<10K --- # textures-color-1k ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The `textures-color-1k` dataset is an image dataset of 1000+ color image textures in 512x512 resolution with associated text descriptions. The dataset was created for training/fine-tuning diffusion models on texture generation tasks. It contains a combination of CC0 procedural and photoscanned PBR materials from [ambientCG](https://ambientcg.com/). ### Languages The text descriptions are in English, and created by joining the tags of each material with a space character. ## Dataset Structure ### Data Instances Each data point contains a 512x512 image and and additional `text` feature containing the description of the texture. ### Data Fields * `image`: the color texture as a PIL image * `text`: the associated text description created by merging the material's tags ### Data Splits \| \| train \| \| -- \| ----- \| \| ambientCG \| 1426 \| ## Dataset Creation ### Curation Rationale `textures-color-1k` was created to provide an accesible source of data for automating 3D-asset creation workflows. The [Dream Textures](https://github.com/carson-katri/dream-textures) add-on is one such tool providing AI automation in Blender. By fine-tuning models such as Stable Diffusion on textures, this particular use-case can be more accurately automated. ### Source Data #### Initial Data Collection and Normalization The data was obtained from [ambientCG](https://ambientcg.com/)'s CC0 textures. Only the color maps were included in this dataset. Text descriptions were synthesized by joining the tags associated with each material with a space. ## Additional Information ### Dataset Curators The dataset was created by Carson Katri, with the images being provided by [ambientCG](https://ambientcg.com/). ### Licensing Information All of the images used in this dataset are CC0. ### Citation Information [N/A] ### Contributions Thanks to [@carson-katri](https://github.com/carson-katri) for adding this dataset.	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open-llm-leaderboard/details_Aspik101__WizardVicuna-Uncensored-3B-instruct-PL-lora_unload	open-llm-leaderboard	2023-09-23T08:35:19Z	300	0	[ "region:us" ]	null	2023-08-18T11:39:23Z	--- pretty_name: Evaluation run of Aspik101/WizardVicuna-Uncensored-3B-instruct-PL-lora_unload dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aspik101/WizardVicuna-Uncensored-3B-instruct-PL-lora_unload](https://huggingface.co/Aspik101/WizardVicuna-Uncensored-3B-instruct-PL-lora_unload)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aspik101__WizardVicuna-Uncensored-3B-instruct-PL-lora_unload\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T08:35:07.670850](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__WizardVicuna-Uncensored-3B-instruct-PL-lora_unload/blob/main/results_2023-09-23T08-35-07.670850.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\": 0.0008389261744966443,\n\ \ \"em_stderr\": 0.0002964962989801231,\n \"f1\": 0.04817009228187932,\n\ \ \"f1_stderr\": 0.0012423575338082733,\n \"acc\": 0.3277999943752083,\n\ \ \"acc_stderr\": 0.007841759236399801\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0008389261744966443,\n \"em_stderr\": 0.0002964962989801231,\n\ \ \"f1\": 0.04817009228187932,\n \"f1_stderr\": 0.0012423575338082733\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.006823351023502654,\n \ \ \"acc_stderr\": 0.0022675371022544753\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.648776637726914,\n \"acc_stderr\": 0.013415981370545126\n\ \ }\n}\n```" repo_url: https://huggingface.co/Aspik101/WizardVicuna-Uncensored-3B-instruct-PL-lora_unload leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|arc:challenge\|25_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-09T14:19:17.231862.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T08_35_07.670850 path: - '/details_harness\|drop\|3_2023-09-23T08-35-07.670850.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-23T08-35-07.670850.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T08_35_07.670850 path: - '/details_harness\|gsm8k\|5_2023-09-23T08-35-07.670850.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-23T08-35-07.670850.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hellaswag\|10_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T14:19:17.231862.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T14:19:17.231862.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T14_19_17.231862 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T14:19:17.231862.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T14:19:17.231862.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T08_35_07.670850 path: - '/details_harness\|winogrande\|5_2023-09-23T08-35-07.670850.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-23T08-35-07.670850.parquet' - config_name: results data_files: - split: 2023_08_09T14_19_17.231862 path: - results_2023-08-09T14:19:17.231862.parquet - split: 2023_09_23T08_35_07.670850 path: - results_2023-09-23T08-35-07.670850.parquet - split: latest path: - results_2023-09-23T08-35-07.670850.parquet --- # Dataset Card for Evaluation run of Aspik101/WizardVicuna-Uncensored-3B-instruct-PL-lora_unload ## Dataset Description - Homepage: - Repository: https://huggingface.co/Aspik101/WizardVicuna-Uncensored-3B-instruct-PL-lora_unload - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Aspik101/WizardVicuna-Uncensored-3B-instruct-PL-lora_unload](https://huggingface.co/Aspik101/WizardVicuna-Uncensored-3B-instruct-PL-lora_unload) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Aspik101__WizardVicuna-Uncensored-3B-instruct-PL-lora_unload", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T08:35:07.670850](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__WizardVicuna-Uncensored-3B-instruct-PL-lora_unload/blob/main/results_2023-09-23T08-35-07.670850.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": 0.0008389261744966443, "em_stderr": 0.0002964962989801231, "f1": 0.04817009228187932, "f1_stderr": 0.0012423575338082733, "acc": 0.3277999943752083, "acc_stderr": 0.007841759236399801 }, "harness\|drop\|3": { "em": 0.0008389261744966443, "em_stderr": 0.0002964962989801231, "f1": 0.04817009228187932, "f1_stderr": 0.0012423575338082733 }, "harness\|gsm8k\|5": { "acc": 0.006823351023502654, "acc_stderr": 0.0022675371022544753 }, "harness\|winogrande\|5": { "acc": 0.648776637726914, "acc_stderr": 0.013415981370545126 } } ``` ### 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]	[ -0.41075757145881653, -0.6599571704864502, 0.08537698537111282, 0.2490747720003128, -0.17436355352401733, 0.08599342405796051, -0.3551931381225586, -0.25291526317596436, 0.3896118402481079, 0.7162376642227173, -0.6543908715248108, -0.8306214809417725, -0.6710795760154724, 0.11664043366909027, -0.332940012216568, 1.1762456893920898, -0.23456615209579468, -0.17265895009040833, 0.02704405039548874, -0.2586170732975006, -0.38154324889183044, -0.34776172041893005, -0.5063867568969727, 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id_puisi	null	2022-11-03T16:08:09Z	299	2	[ "task_categories:text2text-generation", "task_categories:text-generation", "task_categories:fill-mask", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:id", "license:mit", "poem-generation", "region:us" ]	[ "text2text-generation", "text-generation", "fill-mask" ]	2022-03-02T23:29:22Z	--- annotations_creators: - no-annotation language_creators: - found language: - id license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation - text-generation - fill-mask task_ids: [] paperswithcode_id: null pretty_name: Indonesian Puisi tags: - poem-generation dataset_info: features: - name: title dtype: string - name: author dtype: string - name: puisi dtype: string - name: puisi_with_header dtype: string splits: - name: train num_bytes: 10613475 num_examples: 7223 download_size: 10558108 dataset_size: 10613475 --- # Dataset Card for id_puisi ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator) - Repository: [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator) - Paper: [N/A] - Leaderboard: [N/A] - Point of Contact: [Ilham Firdausi Putra]([email protected]) ### Dataset Summary Puisi (poem) is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi with its title and author. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure ### Data Instances ``` { 'puisi_with_header': 'TEPERANGKAP Oleh Mangku Langit Jingga Mungkin kau membiarkan aku Membiarkan perasaan ini larut Memberi ruang jiwaku hampa Agar tetap terbiasa nikmati Perangkap yang kau buat Perisai yang kau banggakan Takkan jadi tameng bagimu Aku mengerti betapa hebatnya Perangkap mu hei sang dewi Ku akan terus merasa terbiasa Dengan pesona indahmu Ku masih akan nikmati hadirmu Berjalanlah pada hati yang sama Satu hati denganku Walau ku terperangkap Namunku nikmati dan jalani', 'title': 'TEPERANGKAP', 'author': 'Oleh Mangku Langit Jingga', 'puisi': 'Mungkin kau membiarkan aku Membiarkan perasaan ini larut Memberi ruang jiwaku hampa Agar tetap terbiasa nikmati Perangkap yang kau buat Perisai yang kau banggakan Takkan jadi tameng bagimu Aku mengerti betapa hebatnya Perangkap mu hei sang dewi Ku akan terus merasa terbiasa Dengan pesona indahmu Ku masih akan nikmati hadirmu Berjalanlah pada hati yang sama Satu hati denganku Walau ku terperangkap Namunku nikmati dan jalani', } ``` ### Data Fields - `puisi_with_header`: the raw text from scraping - `title`: the title extracted from the raw text using regex - `author`: the author extracted from the raw text using regex - `puisi`: the poem with title and author extracted out using regex ### Data Splits The dataset contains only a train set. ## Dataset Creation ### Curation Rationale The dataset was initially collected as an experiment to generate an Indonesian poem using GPT-2. ### Source Data #### Initial Data Collection and Normalization The dataset was scraped using BeautifulSoup from lokerpuisi.web.id (the data no longer exist on the original blog). The title and author column was produced using regex match from puisi_with_header column. #### Who are the source language producers? The poems were generated by humans. The users of the original blog voluntarily submit their original poems to get published on the blog. ### Annotations #### Annotation process [N/A] #### Who are the annotators? [N/A] ### 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 The regex match used to extract the title & author from the raw text is not perfect. Some title & text is still failed to get extracted. ## Additional Information ### Dataset Curators Ilham Firdausi Putra ### Licensing Information MIT License ### Citation Information [N/A] ### Contributions Thanks to [@ilhamfp](https://github.com/ilhamfp) for adding this dataset.	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gigant/african_accented_french	gigant	2022-10-24T17:39:03Z	299	4	[ "task_categories:automatic-speech-recognition", "language:fr", "license:cc", "region:us" ]	[ "automatic-speech-recognition" ]	2022-03-02T23:29:22Z	--- language: - fr license: cc size_categories: fr: - 10K<n<100K task_categories: - automatic-speech-recognition task_ids: [] pretty_name: African Accented French --- ## Dataset Description - Homepage: http://www.openslr.org/57/ ### Dataset Summary This corpus consists of approximately 22 hours of speech recordings. Transcripts are provided for all the recordings. The corpus can be divided into 3 parts: 1. Yaounde Collected by a team from the U.S. Military Academy's Center for Technology Enhanced Language Learning (CTELL) in 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female. 2. CA16 This part was collected by a RDECOM Science Team who participated in the United Nations exercise Central Accord 16 (CA16) in Libreville, Gabon in June 2016. The Science Team included DARPA's Dr. Boyan Onyshkevich and Dr. Aaron Lawson (SRI International), as well as RDECOM scientists. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon. 3. Niger This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015. These speakers were students in a course for officers and sergeants presented by Army trainers assigned to U.S. Army Africa. The data was collected by RDECOM Science & Technology Advisors Major Eddie Strimel and Mr. Bill Bergen. ### Languages French ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, called audio and its sentence. ### Data Fields - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - sentence: The sentence the user was prompted to speak ### Data Splits The speech material has been subdivided into portions for train and test. The train split consists of 9401 audio clips and the related sentences. The test split consists of 1985 audio clips and the related sentences. ### Contributions [@gigant](https://huggingface.co/gigant) added this dataset.	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huggingartists/the-beatles	huggingartists	2022-10-25T09:46:31Z	299	0	[ "language:en", "huggingartists", "lyrics", "region:us" ]	null	2022-03-02T23:29:22Z	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/the-beatles" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 1.07072 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/df75ede64ffcf049727bfbb01d323081.400x400x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/the-beatles"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">The Beatles</div> <a href="https://genius.com/artists/the-beatles"> <div style="text-align: center; font-size: 14px;">@the-beatles</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/the-beatles). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/the-beatles") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|878\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/the-beatles") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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jordyvl/DUDE_loader	jordyvl	2023-10-03T10:54:36Z	299	8	[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "region:us" ]	[ "question-answering" ]	2023-01-24T15:20:01Z	--- license: cc-by-4.0 task_categories: - question-answering language: - en pretty_name: DUDE size_categories: - 10K<n<100K --- ## Loading the dataset with a specific configuration There are 3 different OCR versions to choose from with their original format or standardized DUE format, as well as the option to load the documents as filepaths or as binaries (PDF). To load a specific configuration, pass a config from one of the following: ```python #{bin_}{Amazon,Azure,Tesseract}_{original,due} ['Amazon_due', 'Amazon_original', 'Azure_due', 'Azure_original', 'Tesseract_due', 'Tesseract_original', 'bin_Amazon_due', 'bin_Amazon_original', 'bin_Azure_due', 'bin_Azure_original', 'bin_Tesseract_due', 'bin_Tesseract_original'] ``` Loading the dataset: ```python from datasets import load_dataset ds = load_dataset("jordyvl/DUDE_loader", 'Amazon_original') ``` This dataset repository contains helper functions to convert the dataset to ImDB (image database) format. We advise to clone the repository and run it according to your preferences (OCR version, lowercasing, ...). When running the above data loading script, you should be able to find the extracted binaries under the [HF_CACHE](https://huggingface.co/docs/datasets/cache): `HF_CACHE/datasets/downloads/extracted/<hash>/DUDE_train-val-test_binaries`, which can be reused for the `data_dir` argument. For example: ```bash python3 DUDE_imdb_loader.py \ --data_dir ~/.cache/huggingface/datasets/downloads/extracted/7adde0ed7b0150b7f6b32e52bcad452991fde0f3407c8a87e74b1cb475edaa5b/DUDE_train-val-test_binaries/ ``` For baselines, we recommend having a look at the [MP-DocVQA repository](https://github.com/rubenpt91/MP-DocVQA-Framework) We strongly encourage you to benchmark your best models and submit test set predictions on the [DUDE competition leaderboard](https://rrc.cvc.uab.es/?ch=23) To help with test set predictions, we have included a sample submission file `RRC_DUDE_testset_submission_example.json`.	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camel-ai/physics	camel-ai	2023-05-23T21:12:11Z	299	23	[ "task_categories:text-generation", "language:en", "license:cc-by-nc-4.0", "instruction-finetuning", "arxiv:2303.17760", "region:us" ]	[ "text-generation" ]	2023-04-11T22:49:01Z	--- license: cc-by-nc-4.0 language: - en tags: - instruction-finetuning pretty_name: CAMEL Physics task_categories: - text-generation arxiv: 2303.17760 extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT." extra_gated_fields: Name: text Email: text I will adhere to the terms and conditions of this dataset: checkbox --- # CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society - Github: https://github.com/lightaime/camel - Website: https://www.camel-ai.org/ - Arxiv Paper: https://arxiv.org/abs/2303.17760 ## Dataset Summary Physics dataset is composed of 20K problem-solution pairs obtained using gpt-4. The dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs. We provide the data in `physics.zip`. ## Data Fields The data fields for files in `physics.zip` are as follows: * `role_1`: assistant role * `topic`: physics topic * `sub_topic`: physics subtopic belonging to topic * `message_1`: refers to the problem the assistant is asked to solve. * `message_2`: refers to the solution provided by the assistant. Download in python ``` from huggingface_hub import hf_hub_download hf_hub_download(repo_id="camel-ai/physics", repo_type="dataset", filename="physics.zip", local_dir="datasets/", local_dir_use_symlinks=False) ``` ### Citation ``` @misc{li2023camel, title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society}, author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem}, year={2023}, eprint={2303.17760}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ## Disclaimer: This data was synthetically generated by GPT4 and might contain incorrect information. 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FreedomIntelligence/phoenix-sft-data-v1	FreedomIntelligence	2023-04-23T14:26:36Z	299	16	[ "license:cc-by-4.0", "region:us" ]	null	2023-04-23T13:35:41Z	--- license: cc-by-4.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 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open-llm-leaderboard/details_chansung__gpt4-alpaca-lora-13b-decapoda-1024	open-llm-leaderboard	2023-09-17T14:48:13Z	299	0	[ "region:us" ]	null	2023-08-18T11:08:14Z	--- pretty_name: Evaluation run of chansung/gpt4-alpaca-lora-13b-decapoda-1024 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chansung/gpt4-alpaca-lora-13b-decapoda-1024](https://huggingface.co/chansung/gpt4-alpaca-lora-13b-decapoda-1024)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chansung__gpt4-alpaca-lora-13b-decapoda-1024\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T14:48:01.782551](https://huggingface.co/datasets/open-llm-leaderboard/details_chansung__gpt4-alpaca-lora-13b-decapoda-1024/blob/main/results_2023-09-17T14-48-01.782551.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\": 0.006082214765100671,\n\ \ \"em_stderr\": 0.0007962432393029008,\n \"f1\": 0.06735423657718136,\n\ \ \"f1_stderr\": 0.0015529687729517118,\n \"acc\": 0.42730136257586737,\n\ \ \"acc_stderr\": 0.009642272426310498\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.006082214765100671,\n \"em_stderr\": 0.0007962432393029008,\n\ \ \"f1\": 0.06735423657718136,\n \"f1_stderr\": 0.0015529687729517118\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.08112206216830932,\n \ \ \"acc_stderr\": 0.007520395797922653\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698341\n\ \ }\n}\n```" repo_url: https://huggingface.co/chansung/gpt4-alpaca-lora-13b-decapoda-1024 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|arc:challenge\|25_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-09T17:49:33.952223.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T14_48_01.782551 path: - '/details_harness\|drop\|3_2023-09-17T14-48-01.782551.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T14-48-01.782551.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T14_48_01.782551 path: - '/details_harness\|gsm8k\|5_2023-09-17T14-48-01.782551.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T14-48-01.782551.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hellaswag\|10_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T17:49:33.952223.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-09T17:49:33.952223.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T17_49_33.952223 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T17:49:33.952223.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-09T17:49:33.952223.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T14_48_01.782551 path: - '/details_harness\|winogrande\|5_2023-09-17T14-48-01.782551.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T14-48-01.782551.parquet' - config_name: results data_files: - split: 2023_08_09T17_49_33.952223 path: - results_2023-08-09T17:49:33.952223.parquet - split: 2023_09_17T14_48_01.782551 path: - results_2023-09-17T14-48-01.782551.parquet - split: latest path: - results_2023-09-17T14-48-01.782551.parquet --- # Dataset Card for Evaluation run of chansung/gpt4-alpaca-lora-13b-decapoda-1024 ## Dataset Description - Homepage: - Repository: https://huggingface.co/chansung/gpt4-alpaca-lora-13b-decapoda-1024 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [chansung/gpt4-alpaca-lora-13b-decapoda-1024](https://huggingface.co/chansung/gpt4-alpaca-lora-13b-decapoda-1024) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_chansung__gpt4-alpaca-lora-13b-decapoda-1024", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T14:48:01.782551](https://huggingface.co/datasets/open-llm-leaderboard/details_chansung__gpt4-alpaca-lora-13b-decapoda-1024/blob/main/results_2023-09-17T14-48-01.782551.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": 0.006082214765100671, "em_stderr": 0.0007962432393029008, "f1": 0.06735423657718136, "f1_stderr": 0.0015529687729517118, "acc": 0.42730136257586737, "acc_stderr": 0.009642272426310498 }, "harness\|drop\|3": { "em": 0.006082214765100671, "em_stderr": 0.0007962432393029008, "f1": 0.06735423657718136, "f1_stderr": 0.0015529687729517118 }, "harness\|gsm8k\|5": { "acc": 0.08112206216830932, "acc_stderr": 0.007520395797922653 }, "harness\|winogrande\|5": { "acc": 0.7734806629834254, "acc_stderr": 0.011764149054698341 } } ``` ### 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]	[ -0.48395517468452454, -0.7558497786521912, 0.23127543926239014, 0.28268158435821533, -0.239313542842865, 0.06027446687221527, -0.34908750653266907, -0.2746257185935974, 0.4972321093082428, 0.5357593894004822, -0.702847421169281, -0.9320684671401978, -0.7422859072685242, 0.2618478834629059, -0.2335108518600464, 1.1310921907424927, -0.26203715801239014, -0.29295575618743896, 0.17701689898967743, -0.35453954339027405, -0.4157828986644745, -0.3716229796409607, -0.5640423893928528, 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open-llm-leaderboard/details_bofenghuang__vigogne-2-7b-instruct	open-llm-leaderboard	2023-09-23T08:45:43Z	299	0	[ "region:us" ]	null	2023-08-18T11:33:01Z	--- pretty_name: Evaluation run of bofenghuang/vigogne-2-7b-instruct dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [bofenghuang/vigogne-2-7b-instruct](https://huggingface.co/bofenghuang/vigogne-2-7b-instruct)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bofenghuang__vigogne-2-7b-instruct\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T08:45:31.930950](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-2-7b-instruct/blob/main/results_2023-09-23T08-45-31.930950.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\": 0.2985528523489933,\n\ \ \"em_stderr\": 0.0046864904941642995,\n \"f1\": 0.3518403942953031,\n\ \ \"f1_stderr\": 0.004613402461586294,\n \"acc\": 0.39622289254314186,\n\ \ \"acc_stderr\": 0.008677803422491042\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.2985528523489933,\n \"em_stderr\": 0.0046864904941642995,\n\ \ \"f1\": 0.3518403942953031,\n \"f1_stderr\": 0.004613402461586294\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.03790750568612585,\n \ \ \"acc_stderr\": 0.005260333907798437\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7545382794001578,\n \"acc_stderr\": 0.012095272937183646\n\ \ }\n}\n```" repo_url: https://huggingface.co/bofenghuang/vigogne-2-7b-instruct leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|arc:challenge\|25_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-25T10:36:05.447803.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T08_45_31.930950 path: - '/details_harness\|drop\|3_2023-09-23T08-45-31.930950.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-23T08-45-31.930950.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T08_45_31.930950 path: - '/details_harness\|gsm8k\|5_2023-09-23T08-45-31.930950.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-23T08-45-31.930950.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hellaswag\|10_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-25T10:36:05.447803.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-25T10:36:05.447803.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_25T10_36_05.447803 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-25T10:36:05.447803.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-25T10:36:05.447803.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T08_45_31.930950 path: - '/details_harness\|winogrande\|5_2023-09-23T08-45-31.930950.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-23T08-45-31.930950.parquet' - config_name: results data_files: - split: 2023_07_25T10_36_05.447803 path: - results_2023-07-25T10:36:05.447803.parquet - split: 2023_09_23T08_45_31.930950 path: - results_2023-09-23T08-45-31.930950.parquet - split: latest path: - results_2023-09-23T08-45-31.930950.parquet --- # Dataset Card for Evaluation run of bofenghuang/vigogne-2-7b-instruct ## Dataset Description - Homepage: - Repository: https://huggingface.co/bofenghuang/vigogne-2-7b-instruct - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [bofenghuang/vigogne-2-7b-instruct](https://huggingface.co/bofenghuang/vigogne-2-7b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_bofenghuang__vigogne-2-7b-instruct", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T08:45:31.930950](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-2-7b-instruct/blob/main/results_2023-09-23T08-45-31.930950.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": 0.2985528523489933, "em_stderr": 0.0046864904941642995, "f1": 0.3518403942953031, "f1_stderr": 0.004613402461586294, "acc": 0.39622289254314186, "acc_stderr": 0.008677803422491042 }, "harness\|drop\|3": { "em": 0.2985528523489933, "em_stderr": 0.0046864904941642995, "f1": 0.3518403942953031, "f1_stderr": 0.004613402461586294 }, "harness\|gsm8k\|5": { "acc": 0.03790750568612585, "acc_stderr": 0.005260333907798437 }, "harness\|winogrande\|5": { "acc": 0.7545382794001578, "acc_stderr": 0.012095272937183646 } } ``` ### 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]	[ -0.4002613127231598, -0.6958034634590149, 0.21484477818012238, 0.2797887325286865, -0.21352262794971466, 0.04563027247786522, -0.4359358549118042, -0.1870405226945877, 0.30826354026794434, 0.5691251158714294, -0.6936246752738953, -0.9819462299346924, -0.6350805759429932, 0.12996569275856018, -0.1514626145362854, 1.097044825553894, -0.20136913657188416, -0.1273747980594635, -0.04846290871500969, -0.25261786580085754, -0.38262370228767395, -0.4149189591407776, -0.5353463292121887, 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open-llm-leaderboard/details_pillowtalks-ai__delta13b	open-llm-leaderboard	2023-09-20T15:25:02Z	299	0	[ "region:us" ]	null	2023-08-18T11:54:57Z	--- pretty_name: Evaluation run of pillowtalks-ai/delta13b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [pillowtalks-ai/delta13b](https://huggingface.co/pillowtalks-ai/delta13b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_pillowtalks-ai__delta13b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-20T15:24:50.493097](https://huggingface.co/datasets/open-llm-leaderboard/details_pillowtalks-ai__delta13b/blob/main/results_2023-09-20T15-24-50.493097.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\": 0.029677013422818792,\n\ \ \"em_stderr\": 0.0017378324714143493,\n \"f1\": 0.09310612416107406,\n\ \ \"f1_stderr\": 0.002167792401176146,\n \"acc\": 0.4141695683211732,\n\ \ \"acc_stderr\": 0.010019161585538096\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.029677013422818792,\n \"em_stderr\": 0.0017378324714143493,\n\ \ \"f1\": 0.09310612416107406,\n \"f1_stderr\": 0.002167792401176146\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.08642911296436695,\n \ \ \"acc_stderr\": 0.00774004433710381\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7419100236779794,\n \"acc_stderr\": 0.012298278833972384\n\ \ }\n}\n```" repo_url: https://huggingface.co/pillowtalks-ai/delta13b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|arc:challenge\|25_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-18T13:54:11.410236.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_20T15_24_50.493097 path: - '/details_harness\|drop\|3_2023-09-20T15-24-50.493097.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-20T15-24-50.493097.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_20T15_24_50.493097 path: - '/details_harness\|gsm8k\|5_2023-09-20T15-24-50.493097.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-20T15-24-50.493097.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hellaswag\|10_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T13:54:11.410236.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T13:54:11.410236.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_18T13_54_11.410236 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-18T13:54:11.410236.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-18T13:54:11.410236.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_20T15_24_50.493097 path: - '/details_harness\|winogrande\|5_2023-09-20T15-24-50.493097.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-20T15-24-50.493097.parquet' - config_name: results data_files: - split: 2023_07_18T13_54_11.410236 path: - results_2023-07-18T13:54:11.410236.parquet - split: 2023_09_20T15_24_50.493097 path: - results_2023-09-20T15-24-50.493097.parquet - split: latest path: - results_2023-09-20T15-24-50.493097.parquet --- # Dataset Card for Evaluation run of pillowtalks-ai/delta13b ## Dataset Description - Homepage: - Repository: https://huggingface.co/pillowtalks-ai/delta13b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [pillowtalks-ai/delta13b](https://huggingface.co/pillowtalks-ai/delta13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_pillowtalks-ai__delta13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-20T15:24:50.493097](https://huggingface.co/datasets/open-llm-leaderboard/details_pillowtalks-ai__delta13b/blob/main/results_2023-09-20T15-24-50.493097.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": 0.029677013422818792, "em_stderr": 0.0017378324714143493, "f1": 0.09310612416107406, "f1_stderr": 0.002167792401176146, "acc": 0.4141695683211732, "acc_stderr": 0.010019161585538096 }, "harness\|drop\|3": { "em": 0.029677013422818792, "em_stderr": 0.0017378324714143493, "f1": 0.09310612416107406, "f1_stderr": 0.002167792401176146 }, "harness\|gsm8k\|5": { "acc": 0.08642911296436695, "acc_stderr": 0.00774004433710381 }, "harness\|winogrande\|5": { "acc": 0.7419100236779794, "acc_stderr": 0.012298278833972384 } } ``` ### 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]	[ -0.34083232283592224, -0.7012555599212646, 0.057743653655052185, 0.40940821170806885, -0.13528770208358765, 0.1768629550933838, -0.42843517661094666, -0.2546731233596802, 0.4617643356323242, 0.41192057728767395, -0.7231271266937256, -0.9028132557868958, -0.6601262092590332, 0.13436290621757507, -0.09361948072910309, 1.0964901447296143, -0.08474075049161911, -0.15243655443191528, -0.04908711835741997, -0.20389656722545624, -0.39709630608558655, -0.2827613949775696, -0.598051130771637, 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squarelike/OpenOrca-gugugo-ko	squarelike	2023-11-12T22:41:06Z	299	17	[ "task_categories:conversational", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:10M<n<100M", "language:ko", "license:mit", "arxiv:2306.02707", "arxiv:2301.13688", "region:us" ]	[ "conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation" ]	2023-11-04T05:57:56Z	--- language: - ko license: mit task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation pretty_name: OpenOrca size_categories: - 10M<n<100M --- ![logo](https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1/resolve/main/logo.png) # OpenOrca 한국어 번역 데이터셋 [Gugugo-koen-7B-V1.1](https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1)을 이용하여 [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca)데이터셋을 번역하고 있습니다. 번역 진행상황은 아래를 참고해 주십시오. ## 진행상황 - GPT4 생성물 약 100만 개 중 약 64만 개 번역완료 - GPT3.5 생성물 약 350만 개 중 약 159만 개 번역완료 데이터셋 사용 후 출처표기는 제작자에게 큰 힘이 됩니다. # Original dataset card: OpenOrca ## Table of Contents - [Dataset Summary](#dataset-summary) - [Dataset Attribution](#dataset-attribution) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Dataset Use](#dataset-use) - [Use Cases](#use-cases) - [Usage Caveats](#usage-caveats) - [Getting Started](#getting-started) <p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p> ![OpenOrca Logo](https://huggingface.co/datasets/Open-Orca/OpenOrca/resolve/main/OpenOrcaLogo.png "OpenOrca Logo") <a name="dataset-announcement"></a> We are thrilled to announce the release of the OpenOrca dataset! This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707). It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers! # Official Models ## Mistral-7B-OpenOrca Our [latest model](https://huggingface.co/spaces/Open-Orca/Mistral-7B-OpenOrca), the first 7B to score better overall than all previous models below 30B. 98% of Llama2-70b-chat's performance, in a completely open 7B! ## OpenOrca-Platypus2-13B Our [third model](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard! Released in partnership with Platypus. ## LlongOrca 7B & 13B * Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance. * [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance. ## OpenOrcaxOpenChat-Preview2-13B Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper. Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B. Released in partnership with OpenChat. ## OpenOrca-Preview1-13B [OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B) This model was trained in less than a day, for <$200, with <10% of our data. At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper. <a name="dataset-summary"></a> # Dataset Summary The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688). Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions. It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope. The data is primarily used for training and evaluation in the field of natural language processing. <a name="dataset-attribution"></a> # Dataset Attribution We would like to give special recognition to the following contributors for their significant efforts and dedication: Teknium WingLian/Caseus Eric Hartford NanoBit Pankaj Winddude Rohan http://AlignmentLab.ai: Autometa Entropi AtlasUnified NeverendingToast NanoBit WingLian/Caseus Also of course, as always, TheBloke, for being the backbone of the whole community. Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials: http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2). [<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2) <a name="supported-tasks-and-leaderboards"></a> # Supported Tasks and Leaderboards This dataset supports a range of tasks including language modeling, text generation, and text augmentation. It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing. Further information on leaderboards will be updated as they become available. <a name="languages"></a> # Languages The language of the data is primarily English. <a name="dataset-structure"></a> # Dataset Structure <a name="data-instances"></a> ## Data Instances A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5. The response is then entered into the response field. <a name="data-fields"></a> ## Data Fields The fields are: 1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from. 2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint 3) 'question', representing a question entry as provided by the FLAN Collection 4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4. <a name="data-splits"></a> ## Data Splits The data is unsplit. <a name="dataset-creation"></a> # Dataset Creation <a name="curation-rationale"></a> ## Curation Rationale The dataset was created to provide a source of augmented text data for researchers and developers. The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4. This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on. <a name="source-data"></a> ## Source Data The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below: 1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use. We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available. 2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original). These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source. However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively. Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work. <a name="dataset-use"></a> # Dataset Use <a name="use-cases"></a> ## Use Cases The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation. <a name="usage-caveats"></a> ## Usage Caveats Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements. Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper. <a name="getting-started"></a> ## Getting Started This dataset is organized such that it can be naively loaded via Hugging Face datasets library. We recommend using streaming due to the large size of the files. Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face. # Citation ```bibtex @misc{OpenOrca, title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces}, author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca}}, } ``` ```bibtex @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint= arXiv 2307.09288 } @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```	[ -0.5827696919441223, 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atomic	null	2022-11-18T18:56:37Z	298	6	[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "common-sense-if-then-reasoning", "region:us" ]	[ "text2text-generation" ]	2022-03-02T23:29:22Z	--- pretty_name: ATOMIC annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: atomic tags: - common-sense-if-then-reasoning dataset_info: features: - name: event dtype: string - name: oEffect sequence: string - name: oReact sequence: string - name: oWant sequence: string - name: xAttr sequence: string - name: xEffect sequence: string - name: xIntent sequence: string - name: xNeed sequence: string - name: xReact sequence: string - name: xWant sequence: string - name: prefix sequence: string - name: split dtype: string config_name: atomic splits: - name: train num_bytes: 32441878 num_examples: 202271 - name: test num_bytes: 3995624 num_examples: 24856 - name: validation num_bytes: 3629768 num_examples: 22620 download_size: 19083782 dataset_size: 40067270 --- # Dataset Card for An Atlas of Machine Commonsense for If-Then Reasoning - Atomic Common Sense Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://homes.cs.washington.edu/~msap/atomic/ - Repository: https://homes.cs.washington.edu/~msap/atomic/ - Paper: Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith & Yejin Choi (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI ### Dataset Summary This dataset provides the template sentences and relationships defined in the ATOMIC common sense dataset. There are three splits - train, test, and dev. From the authors. Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us ([email protected]) if you have any concerns. For more information, see: https://homes.cs.washington.edu/~msap/atomic/ ### Supported Tasks and Leaderboards [More Information Needed] ### Languages en ## Dataset Structure ### Data Instances Here is one example from the atomic dataset: `` {'event': "PersonX uses PersonX's ___ to obtain", 'oEffect': [], 'oReact': ['annoyed', 'angry', 'worried'], 'oWant': [], 'prefix': ['uses', 'obtain'], 'split': 'trn', 'xAttr': [], 'xEffect': [], 'xIntent': ['to have an advantage', 'to fulfill a desire', 'to get out of trouble'], 'xNeed': [], 'xReact': ['pleased', 'smug', 'excited'], 'xWant': []} `` ### Data Fields Notes from the authors: * event: just a string representation of the event. * oEffect,oReact,oWant,xAttr,xEffect,xIntent,xNeed,xReact,xWant: annotations for each of the dimensions, stored in a json-dumped string. Note: "none" means the worker explicitly responded with the empty response, whereas [] means the worker did not annotate this dimension. * prefix: json-dumped string that represents the prefix of content words (used to make a better trn/dev/tst split). * split: string rep of which split the event belongs to. ### Data Splits The atomic dataset has three splits: test, train and dev of the form: ## Dataset Creation ### Curation Rationale This dataset was gathered and created over to assist in common sense reasoning. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created by the University of Washington using crowd sourced data #### Who are the source language producers? The Atomic authors and crowd source. ### Annotations #### Annotation process Human annotations directed by forms. #### Who are the annotators? Human annotations. ### Personal and Sensitive Information Unkown, but likely none. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines understand common sense. ### Discussion of Biases Since the data is human annotators, there is likely to be baised. From the authors: Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us ([email protected]) if you have any concerns. ### Other Known Limitations While there are many relationships, the data is quite sparse. Also, each item of the dataset could be expanded into multiple sentences along the vsrious dimensions, oEffect, oRect, etc. For example, given event: "PersonX uses PersonX's ___ to obtain" and dimension oReact: "annoyed", this could be transformed into an entry: "PersonX uses PersonX's ___ to obtain => PersonY is annoyed" ## Additional Information ### Dataset Curators The authors of Aotmic at The University of Washington ### Licensing Information The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/ ### Citation Information @article{Sap2019ATOMICAA, title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning}, author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi}, journal={ArXiv}, year={2019}, volume={abs/1811.00146} } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.	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newsph_nli	null	2023-01-25T14:41:24Z	298	0	[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:tl", "license:unknown", "arxiv:2010.11574", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	--- annotations_creators: - machine-generated language_creators: - found language: - tl license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: newsph-nli pretty_name: NewsPH NLI dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 154510599 num_examples: 420000 - name: test num_bytes: 3283665 num_examples: 9000 - name: validation num_bytes: 33015530 num_examples: 90000 download_size: 76565287 dataset_size: 190809794 --- # Dataset Card for NewsPH NLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [NewsPH NLI homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - Repository: [NewsPH NLI repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - Paper: [Arxiv paper](https://arxiv.org/pdf/2010.11574.pdf) - Leaderboard: - Point of Contact: [Jan Christian Cruz](mailto:[email protected]) ### Dataset Summary First benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset contains news articles in Filipino (Tagalog) scraped rom all major Philippine news sites online. ## Dataset Structure ### Data Instances Sample data: { "premise": "Alam ba ninyo ang ginawa ni Erap na noon ay lasing na lasing na rin?", "hypothesis": "Ininom niya ang alak na pinagpulbusan!", "label": "0" } ### Data Fields [More Information Needed] ### Data Splits Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing. ## Dataset Creation ### Curation Rationale We propose the use of news articles for automatically creating benchmark datasets for NLI because of two reasons. First, news articles commonly use single-sentence paragraphing, meaning every paragraph in a news article is limited to a single sentence. Second, straight news articles follow the “inverted pyramid” structure, where every succeeding paragraph builds upon the premise of those that came before it, with the most important information on top and the least important towards the end. ### Source Data #### Initial Data Collection and Normalization To create the dataset, we scrape news articles from all major Philippine news sites online. We collect a total of 229,571 straight news articles, which we then lightly preprocess to remove extraneous unicode characters and correct minimal misspellings. No further preprocessing is done to preserve information in the data. #### Who are the source language producers? The dataset was created by Jan Christian, Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco, and Charibeth Cheng from De La Salle University and the University of the Philippines ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Jan Christian Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco and Charibeth Cheng ### 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 [Jan Christian Blaise Cruz] (mailto:[email protected]) ### Licensing Information [More Information Needed] ### Citation Information @article{cruz2020investigating, title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation}, author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng}, journal={arXiv preprint arXiv:2010.11574}, year={2020} } ### Contributions Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset.	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Nexdata/mixed_speech_chinese_english	Nexdata	2023-11-22T09:52:13Z	298	4	[ "task_categories:automatic-speech-recognition", "language:zh", "language:en", "region:us" ]	[ "automatic-speech-recognition" ]	2022-03-02T23:29:22Z	--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging task_categories: - automatic-speech-recognition language: - zh - en --- # Dataset Card for mixed_speech_chinese_english ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://nexdata.ai/?source=Huggingface - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains 2,000 hours of mixed speech with Chinese and English. The data is collected from speakers in 26 provinces like Henan, Shanxi, Sichuan, Hunan, Fujian, etc.The content covers generic scene and multiple human machine interation scenes, such as music, entertainment, travel, daily life. The data covers more than 30,000 English words. The sentence accuracy is over 97%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Chinese, English ## 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 Commercial License ### Citation Information [More Information Needed] ### Contributions	[ -0.31319406628608704, -0.6049943566322327, -0.22426290810108185, 0.5610816478729248, -0.26573631167411804, 0.010267328470945358, -0.4114198684692383, -0.477541983127594, 0.5275148153305054, 0.5691367983818054, -0.6687690615653992, -0.8778836727142334, -0.4536254107952118, 0.0037786434404551983, -0.33860835433006287, 0.9743054509162903, 0.17384418845176697, 0.19735871255397797, 0.05590565875172615, -0.36008045077323914, -0.527209997177124, -0.642381489276886, -0.8983251452445984, -0.5344719886779785, 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teven/enwiki_10k	teven	2023-04-03T14:00:51Z	298	0	[ "region:us" ]	null	2023-04-03T14:00:46Z	--- dataset_info: features: - name: metadata dtype: string - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 25120962 num_examples: 10000 download_size: 15208428 dataset_size: 25120962 --- # Dataset Card for "enwiki_10k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.7803019881248474, -0.1915995329618454, 0.059056058526039124, 0.39017215371131897, -0.19657348096370697, -0.175519660115242, 0.027720419690012932, -0.2681557238101959, 1.0335581302642822, 0.5302069187164307, -0.8149653673171997, -0.5898078083992004, -0.5816460251808167, 0.2404891401529312, -0.2622901499271393, 1.1780918836593628, 0.020191336050629616, -0.13743866980075836, -0.7302102446556091, -0.178532212972641, -0.2646324038505554, -0.13075308501720428, -0.7235457301139832, -0.6424785852432251, 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skeskinen/TinyStories-GPT4	skeskinen	2023-05-20T19:00:22Z	298	15	[ "region:us" ]	null	2023-05-20T18:58:41Z	--- dataset_info: features: - name: story dtype: string - name: summary dtype: string - name: source dtype: string - name: prompt dtype: string - name: words sequence: string - name: features sequence: string splits: - name: train num_bytes: 3680196493 num_examples: 2745100 download_size: 1553670972 dataset_size: 3680196493 --- # Dataset Card for "TinyStories-GPT4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6648578643798828, -0.0766332745552063, 0.5324500799179077, 0.05566989630460739, -0.29312247037887573, -0.17491425573825836, 0.2608069181442261, -0.042772844433784485, 0.6792071461677551, 0.17729470133781433, -0.8959084153175354, -0.6044764518737793, -0.4729979336261749, 0.02080206200480461, -0.014425422996282578, 1.4186875820159912, 0.1089598536491394, -0.0055703939869999886, 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open-llm-leaderboard/details_lmsys__vicuna-7b-delta-v1.1	open-llm-leaderboard	2023-10-12T14:41:09Z	298	0	[ "region:us" ]	null	2023-08-18T11:07:13Z	--- pretty_name: Evaluation run of lmsys/vicuna-7b-delta-v1.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lmsys/vicuna-7b-delta-v1.1](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 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_lmsys__vicuna-7b-delta-v1.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-12T14:40:56.820234](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-delta-v1.1/blob/main/results_2023-10-12T14-40-56.820234.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\": 0.11388422818791946,\n\ \ \"em_stderr\": 0.00325324428862373,\n \"f1\": 0.16976719798657605,\n\ \ \"f1_stderr\": 0.003380156230610554,\n \"acc\": 0.38244753834582057,\n\ \ \"acc_stderr\": 0.009528517622122097\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.11388422818791946,\n \"em_stderr\": 0.00325324428862373,\n\ \ \"f1\": 0.16976719798657605,\n \"f1_stderr\": 0.003380156230610554\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.05534495830174375,\n \ \ \"acc_stderr\": 0.006298221796179588\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7095501183898973,\n \"acc_stderr\": 0.012758813448064607\n\ \ }\n}\n```" repo_url: https://huggingface.co/lmsys/vicuna-7b-delta-v1.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|arc:challenge\|25_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|arc:challenge\|25_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-03T12:35:58.134991.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_12T14_40_56.820234 path: - '/details_harness\|drop\|3_2023-10-12T14-40-56.820234.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-12T14-40-56.820234.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_12T14_40_56.820234 path: - '/details_harness\|gsm8k\|5_2023-10-12T14-40-56.820234.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-12T14-40-56.820234.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hellaswag\|10_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hellaswag\|10_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T12:22:17.969682.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-03T12:35:58.134991.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-03T12:35:58.134991.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_18T12_22_17.969682 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-18T12:22:17.969682.parquet' - split: 2023_08_03T12_35_58.134991 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-03T12:35:58.134991.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-03T12:35:58.134991.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_12T14_40_56.820234 path: - '/details_harness\|winogrande\|5_2023-10-12T14-40-56.820234.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-12T14-40-56.820234.parquet' - config_name: results data_files: - split: 2023_07_18T12_22_17.969682 path: - results_2023-07-18T12:22:17.969682.parquet - split: 2023_08_03T12_35_58.134991 path: - results_2023-08-03T12:35:58.134991.parquet - split: 2023_10_12T14_40_56.820234 path: - results_2023-10-12T14-40-56.820234.parquet - split: latest path: - results_2023-10-12T14-40-56.820234.parquet --- # Dataset Card for Evaluation run of lmsys/vicuna-7b-delta-v1.1 ## Dataset Description - Homepage: - Repository: https://huggingface.co/lmsys/vicuna-7b-delta-v1.1 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [lmsys/vicuna-7b-delta-v1.1](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 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_lmsys__vicuna-7b-delta-v1.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-12T14:40:56.820234](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-delta-v1.1/blob/main/results_2023-10-12T14-40-56.820234.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": 0.11388422818791946, "em_stderr": 0.00325324428862373, "f1": 0.16976719798657605, "f1_stderr": 0.003380156230610554, "acc": 0.38244753834582057, "acc_stderr": 0.009528517622122097 }, "harness\|drop\|3": { "em": 0.11388422818791946, "em_stderr": 0.00325324428862373, "f1": 0.16976719798657605, "f1_stderr": 0.003380156230610554 }, "harness\|gsm8k\|5": { "acc": 0.05534495830174375, "acc_stderr": 0.006298221796179588 }, "harness\|winogrande\|5": { "acc": 0.7095501183898973, "acc_stderr": 0.012758813448064607 } } ``` ### 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]	[ -0.437483549118042, -0.6220721006393433, 0.28813305497169495, 0.24553458392620087, -0.30031880736351013, -0.022821620106697083, -0.31704190373420715, -0.16400979459285736, 0.4826391339302063, 0.5411213636398315, -0.7454520463943481, -0.9573811292648315, -0.600394070148468, 0.1952100396156311, -0.11123427003622055, 1.0142269134521484, -0.23732924461364746, -0.09962254017591476, 0.10470709204673767, -0.3014809489250183, -0.4500366747379303, -0.48772892355918884, -0.6145447492599487, 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open-llm-leaderboard/details_BreadAi__StoryPy	open-llm-leaderboard	2023-09-23T10:16:47Z	298	0	[ "region:us" ]	null	2023-08-18T11:35:44Z	--- pretty_name: Evaluation run of BreadAi/StoryPy dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BreadAi/StoryPy](https://huggingface.co/BreadAi/StoryPy) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BreadAi__StoryPy\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T10:16:36.157284](https://huggingface.co/datasets/open-llm-leaderboard/details_BreadAi__StoryPy/blob/main/results_2023-09-23T10-16-36.157284.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\": 0.0007340604026845638,\n\ \ \"em_stderr\": 0.00027736144573356746,\n \"f1\": 0.011790058724832235,\n\ \ \"f1_stderr\": 0.0007354126826155291,\n \"acc\": 0.255327545382794,\n\ \ \"acc_stderr\": 0.007024647268145198\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.00027736144573356746,\n\ \ \"f1\": 0.011790058724832235,\n \"f1_stderr\": 0.0007354126826155291\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.510655090765588,\n\ \ \"acc_stderr\": 0.014049294536290396\n }\n}\n```" repo_url: https://huggingface.co/BreadAi/StoryPy leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|arc:challenge\|25_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T10:25:02.732559.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T10_16_36.157284 path: - '/details_harness\|drop\|3_2023-09-23T10-16-36.157284.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-23T10-16-36.157284.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T10_16_36.157284 path: - '/details_harness\|gsm8k\|5_2023-09-23T10-16-36.157284.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-23T10-16-36.157284.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hellaswag\|10_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T10:25:02.732559.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T10:25:02.732559.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T10_25_02.732559 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T10:25:02.732559.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T10:25:02.732559.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T10_16_36.157284 path: - '/details_harness\|winogrande\|5_2023-09-23T10-16-36.157284.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-23T10-16-36.157284.parquet' - config_name: results data_files: - split: 2023_07_19T10_25_02.732559 path: - results_2023-07-19T10:25:02.732559.parquet - split: 2023_09_23T10_16_36.157284 path: - results_2023-09-23T10-16-36.157284.parquet - split: latest path: - results_2023-09-23T10-16-36.157284.parquet --- # Dataset Card for Evaluation run of BreadAi/StoryPy ## Dataset Description - Homepage: - Repository: https://huggingface.co/BreadAi/StoryPy - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [BreadAi/StoryPy](https://huggingface.co/BreadAi/StoryPy) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_BreadAi__StoryPy", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T10:16:36.157284](https://huggingface.co/datasets/open-llm-leaderboard/details_BreadAi__StoryPy/blob/main/results_2023-09-23T10-16-36.157284.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": 0.0007340604026845638, "em_stderr": 0.00027736144573356746, "f1": 0.011790058724832235, "f1_stderr": 0.0007354126826155291, "acc": 0.255327545382794, "acc_stderr": 0.007024647268145198 }, "harness\|drop\|3": { "em": 0.0007340604026845638, "em_stderr": 0.00027736144573356746, "f1": 0.011790058724832235, "f1_stderr": 0.0007354126826155291 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.510655090765588, "acc_stderr": 0.014049294536290396 } } ``` ### 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]	[ -0.284279465675354, -0.6322288513183594, 0.2618004381656647, 0.2216593623161316, -0.06328320503234863, 0.18580196797847748, -0.48470768332481384, -0.15087978541851044, 0.4350242018699646, 0.5085974931716919, -0.6842193007469177, -0.9024001359939575, -0.7625558972358704, 0.1293746531009674, -0.14961454272270203, 1.2265840768814087, -0.3076794445514679, -0.24725566804409027, -0.03169560059905052, -0.2850286364555359, -0.3144635558128357, -0.3937053084373474, -0.4868323802947998, 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open-llm-leaderboard/details_FabbriSimo01__Facebook_opt_1.3b_Quantized	open-llm-leaderboard	2023-09-18T06:09:34Z	298	0	[ "region:us" ]	null	2023-08-18T12:01:11Z	--- pretty_name: Evaluation run of FabbriSimo01/Facebook_opt_1.3b_Quantized dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FabbriSimo01/Facebook_opt_1.3b_Quantized](https://huggingface.co/FabbriSimo01/Facebook_opt_1.3b_Quantized)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FabbriSimo01__Facebook_opt_1.3b_Quantized\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-18T06:09:22.891569](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__Facebook_opt_1.3b_Quantized/blob/main/results_2023-09-18T06-09-22.891569.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\": 0.0020973154362416107,\n\ \ \"em_stderr\": 0.00046850650303682405,\n \"f1\": 0.05110318791946325,\n\ \ \"f1_stderr\": 0.0012507542097710141,\n \"acc\": 0.29910069155018665,\n\ \ \"acc_stderr\": 0.007429518317222754\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.00046850650303682405,\n\ \ \"f1\": 0.05110318791946325,\n \"f1_stderr\": 0.0012507542097710141\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.001516300227445034,\n \ \ \"acc_stderr\": 0.0010717793485492619\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5966850828729282,\n \"acc_stderr\": 0.013787257285896245\n\ \ }\n}\n```" repo_url: https://huggingface.co/FabbriSimo01/Facebook_opt_1.3b_Quantized leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|arc:challenge\|25_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T14:58:20.478747.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_18T06_09_22.891569 path: - '/details_harness\|drop\|3_2023-09-18T06-09-22.891569.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-18T06-09-22.891569.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_18T06_09_22.891569 path: - '/details_harness\|gsm8k\|5_2023-09-18T06-09-22.891569.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-18T06-09-22.891569.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hellaswag\|10_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T14:58:20.478747.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T14:58:20.478747.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T14_58_20.478747 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T14:58:20.478747.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T14:58:20.478747.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_18T06_09_22.891569 path: - '/details_harness\|winogrande\|5_2023-09-18T06-09-22.891569.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-18T06-09-22.891569.parquet' - config_name: results data_files: - split: 2023_07_19T14_58_20.478747 path: - results_2023-07-19T14:58:20.478747.parquet - split: 2023_09_18T06_09_22.891569 path: - results_2023-09-18T06-09-22.891569.parquet - split: latest path: - results_2023-09-18T06-09-22.891569.parquet --- # Dataset Card for Evaluation run of FabbriSimo01/Facebook_opt_1.3b_Quantized ## Dataset Description - Homepage: - Repository: https://huggingface.co/FabbriSimo01/Facebook_opt_1.3b_Quantized - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [FabbriSimo01/Facebook_opt_1.3b_Quantized](https://huggingface.co/FabbriSimo01/Facebook_opt_1.3b_Quantized) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_FabbriSimo01__Facebook_opt_1.3b_Quantized", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-18T06:09:22.891569](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__Facebook_opt_1.3b_Quantized/blob/main/results_2023-09-18T06-09-22.891569.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": 0.0020973154362416107, "em_stderr": 0.00046850650303682405, "f1": 0.05110318791946325, "f1_stderr": 0.0012507542097710141, "acc": 0.29910069155018665, "acc_stderr": 0.007429518317222754 }, "harness\|drop\|3": { "em": 0.0020973154362416107, "em_stderr": 0.00046850650303682405, "f1": 0.05110318791946325, "f1_stderr": 0.0012507542097710141 }, "harness\|gsm8k\|5": { "acc": 0.001516300227445034, "acc_stderr": 0.0010717793485492619 }, "harness\|winogrande\|5": { "acc": 0.5966850828729282, "acc_stderr": 0.013787257285896245 } } ``` ### 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]	[ -0.4142058193683624, -0.8356881141662598, 0.23002247512340546, 0.29674777388572693, -0.10855601727962494, 0.13542810082435608, -0.3048042058944702, -0.07717055082321167, 0.4146263599395752, 0.5169322490692139, -0.6772983074188232, -0.9927697777748108, -0.6295410990715027, 0.10950818657875061, -0.19509796798229218, 1.1753913164138794, -0.2565160393714905, -0.24857625365257263, 0.002655886812135577, -0.30126163363456726, -0.14978612959384918, -0.4470183253288269, -0.6668764352798462, 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GEM/totto	GEM	2022-10-24T15:30:32Z	297	1	[ "task_categories:table-to-text", "annotations_creators:none", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "data-to-text", "arxiv:1603.07771", "arxiv:2007.02871", "arxiv:2005.10433", "region:us" ]	[ "table-to-text" ]	2022-03-02T23:29:22Z	--- annotations_creators: - none language_creators: - unknown language: - en license: - cc-by-sa-3.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: totto tags: - data-to-text --- # Dataset Card for GEM/totto ## Dataset Description - Homepage: n/a - Repository: https://github.com/google-research-datasets/totto + [ToTTo Supplementary Repo - Paper: https://aclanthology.org/2020.emnlp-main.89 - Leaderboard: https://github.com/google-research-datasets/totto - Point of Contact: Ankur Parikh ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/totto). ### Dataset Summary ToTTo is a high-quality English table-to-text dataset with more than 100,000 examples in which a table from Wikipedia with highlighted cells is paired with a sentence that describes the highlighted cells. All examples in the dataset were post-edited in multiple steps to ensure that the targets are fully faithful to the input information. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/totto') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/totto). #### website n/a #### paper [ACL Anthology](https://aclanthology.org/2020.emnlp-main.89) #### authors Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das ## Dataset Overview ### Where to find the Data and its Documentation #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [ToTTo Main Repo](https://github.com/google-research-datasets/totto) + [ToTTo Supplementary Repo](https://github.com/google-research/language/tree/master/language/totto) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [ACL Anthology](https://aclanthology.org/2020.emnlp-main.89) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{parikh-etal-2020-totto, title = "{ToTTo}: A Controlled Table-To-Text Generation Dataset", author = "Parikh, Ankur and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.89", doi = "10.18653/v1/2020.emnlp-main.89", pages = "1173--1186", abstract = "We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Ankur Parikh #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> [email protected] #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> yes #### Leaderboard Link <!-- info: Provide a link to the leaderboard. --> <!-- scope: periscope --> [Github](https://github.com/google-research-datasets/totto) #### Leaderboard Details <!-- info: Briefly describe how the leaderboard evaluates models. --> <!-- scope: microscope --> This dataset has an associated, active [leaderboard](https://github.com/google-research-datasets/totto#leaderboard) maintained by the authors. The test set ground truth targets / references are private, i.e they are not publicly shared or downloadable - hence, leaderboard submission is necessary for test set evaluation. To evaluate your model on the dev or test set AND/OR submit to the leaderboard, you need to submit your model files through this [form](https://forms.gle/AcF9TRqWrPhPzztt7) (The form provides an option to opt-out of going on the leaderboard). The leaderboard reports three sets of BLEU, PARENT and BLEURT scores for each submission - on the overall test set, the Overlap subset of the test set and the non-Overlap subset of the test set. ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Dialects <!-- info: What dialects are covered? Are there multiple dialects per language? --> <!-- scope: periscope --> No specific dialects. The original language is from Wikipedia and it was post-edited by crowdraters #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### Whose Language? <!-- info: Whose language is in the dataset? --> <!-- scope: periscope --> The language is post-edited English only (BCP-47: `en`) Wikipedia text. No demographic information about annotators is provided. Some amounts of what may be called non-English text, including characters such as French accents or Cyrillic characters, could sometimes occur, especially through fields with entity names as values in the input table cells. #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> ToTTo is a Table-to-Text NLG task, as the paper title says. The task is as follows: Given a Wikipedia table with row names, column names and table cells, with a subset of cells highlighted, generate a natural language description for the highlighted part of the table . The table need not be exactly rectangular in that - cells can sometimes be multi-row or multi-column. An earlier example of a Table-to-Text NLG task is [Wikibio](https://arxiv.org/abs/1603.07771) - here the inputs were Wikipedia infoboxes (from the top right corner of entity-related Wiki pages). In contrast, ToTTo mostly has Wikipedia tables from the main article content itself. In general, Table-To-Text NLG tasks can be seen as a subclass of Data-To-Text NLG tasks - where the task is to generate natural language descriptions of inputs which are in the form of structured or semi-structured data. In general, all Data-To-Text NLG tasks need not have an explicit table or other structure - e.g the input in [WebNLG](https://www.aclweb.org/anthology/W16-6626.pdf) is simply a list of triples. Importantly, ToTTo differs from earlier examples of Table-To-Text NLG in that: 1. It does not suffer from the problem of divergent references - where ground truth descriptions themselves have additional information not found in the table. ToTTo overcomes this by having a multi-step annotation process to edit the initial, free-form table descriptions (which are from Wikipedia) to make them faithful, unambiguous and independent of article context. 2. Since it provides control in the form of highlighted table cells, it prevents the problem of there being a large number of valid descriptions focussing on different parts of the table. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Data-to-Text #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> The speaker is required to produce a single, coherent English sentence that describes the highlighted cells in the given table, also using metadata and any other information from the table as applicable. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `industry` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Google Research #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> Google Research #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Varun Gangal created the initial data card and Yacine Jernite wrote the data loader. The data card was updated with new splits by Simon Mille. Sebastian Gehrmann ported the data card and loader from the v1 to the v2 version and extended it with the new fields. ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - The `table` field is a `List[List[Dict]]` in row-major order, with outer lists representing rows and the inner lists columns. - Each `Dict` has the fields `column_span: int`, `is_header: bool`, `row_span: int`, and `value: str`. - Table metadata consists of `table_page_title`, `table_section_title` and `table_section_texts` - The `highlighted_cells` are represented as `List[[row_index,column_index]]`, with each `[row_index,column_index]` indicating that `table[row_index][column_index]` is highlighted. - `example_id` is the unique id per example. - `sentence_annotations[final_sentence]` which is the table description/generation target #### Reason for Structure <!-- info: How was the dataset structure determined? --> <!-- scope: microscope --> The structure is aimed to encode highlighted tables in a way that allows rows and columns to span multiple fields in width. The other fields are meta-data about the source and the annotations #### How were labels chosen? <!-- info: How were the labels chosen? --> <!-- scope: microscope --> The initial table-description pairs are tables from Wikipedia articles, extracted through heuristics such as Number Matching (tables and sentences that overlap with a non-date number of atleast 3 non-zero digits) (Refer to Section 4 of the paper for more) 1. Table Readability: Tables which are deemed non-readable (due to foreign language, poor formatting etc - a very small fraction of 0.5%) are removed from the dataset here. 2. Cell Highlighting: The annotator highlights the cells of the table which support the description. 3. Deletion: The annotator removes phrases in the description which are not supported by the highlighted cells 4. Decontextualization: Descriptions may contain pronouns or other forms of anaphora, or other phenomena which depend on the overall article topic - these are fixed by replacement (e.g replacing pronouns with the entity, provided it occurs in the table). The replacements allowed are limited to one, and annotators are also instructed to conserve fluency. 5. Secondary Annotation: A second set of annotators is shown the output of Stage 4, and asked to fix it if required to ensure it is grammatical. #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> The main repository's `README.md` already provides a thorough walkthrough of data instances and fields [here](https://github.com/google-research-datasets/totto#dataset-description) Below is the instance for a table from the wiki-page for the musical artist _Weird Al' Yankovic_ , likely listing his on-television appearances. ``` { "table_page_title": "'Weird Al' Yankovic", "table_webpage_url": "https://en.wikipedia.org/wiki/%22Weird_Al%22_Yankovic", "table_section_title": "Television", "table_section_text": "", "table": "[Described below]", "highlighted_cells": [[22, 2], [22, 3], [22, 0], [22, 1], [23, 3], [23, 1], [23, 0]], "example_id": 12345678912345678912, "sentence_annotations": [{"original_sentence": "In 2016, Al appeared in 2 episodes of BoJack Horseman as Mr. Peanutbutter's brother, Captain Peanutbutter, and was hired to voice the lead role in the 2016 Disney XD series Milo Murphy's Law.", "sentence_after_deletion": "In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter, and was hired to the lead role in the 2016 series Milo Murphy's Law.", "sentence_after_ambiguity": "In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter, and was hired for the lead role in the 2016 series Milo Murphy's 'Law.", "final_sentence": "In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter and was hired for the lead role in the 2016 series Milo Murphy's Law."}], } ``` The `table` field is expanded as below: ``` [ [ { "column_span": 1, "is_header": true, "row_span": 1, "value": "Year"}, { "column_span": 1, "is_header": true, "row_span": 1, "value": "Title"}, { "column_span": 1, "is_header": true, "row_span": 1, "value": "Role"}, { "column_span": 1, "is_header": true, "row_span": 1, "value": "Notes"} ], [ { "column_span": 1, "is_header": false, "row_span": 1, "value": "1997"}, { "column_span": 1, "is_header": false, "row_span": 1, "value": "Eek! The Cat"}, { "column_span": 1, "is_header": false, "row_span": 1, "value": "Himself"}, { "column_span": 1, "is_header": false, "row_span": 1, "value": "Episode: 'The FugEektive'"} ], ... ] ``` The [Supplementary Repo](https://github.com/google-research/language/tree/master/language/totto) also provides browsable samples under its `sample/` folder. It additionally provides HTML visualization scripts with their outputs located under the aforementioned folder. The instructions to access and visualize these samples can also be found [here](https://github.com/google-research/language/tree/master/language/totto#visualizing-sample-data). #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> The dataset consists of 120,000 train examples and equi-sized dev and test sets with 7700 examples. Refer to Table 5 in the paper for a more extensive list of properties about table size, target vocabulary etc and their aggregates. #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The dev and test splits are further equally distributed between _Overlap_ and _non-Overlap_ . The examples in the _Overlap_ set are harder on account of the domain shift resulting from them having none of their header (row and column) names in common with those seen during training. Refer to Table 5 in the paper for a more extensive list of properties about table size, target vocabulary etc and their aggregates. #### <!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? --> <!-- scope: microscope --> There are some very large tables in the dataset with thousands of rows. Table 7 shows some of the challenges of the dataset, showing that very few examples require access to the table description itself which makes those examples an outlier. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> ToTTo is one of the two datasets representing Table-to-Text NLG in GEM, the other one being [DART](https://arxiv.org/pdf/2007.02871.pdf). Unlike DART, which combines datasets from multiple sources and furnishes them in a unified setting, ToTTo is from a homogeneous source. As explained in the Task Summary above, it also has an annotation process explicitly crafted to reduce divergent descriptions, which is not true of DART. Furthermore, ToTTo is also an instance of a controlled generation task - where in addition to the input (in this case the table) an additional control (in this case the highlighted cells) is given as an additional goal for the generation. The DART task formulation does not include controls. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> The input is much more complex and the quality much better than that of comparable datasets. The highlighted table cells provide a unique challenge to models. #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> Reasoning, surface realization ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> yes #### Split Information <!-- info: Describe how the new splits were created --> <!-- scope: periscope --> 9 challenge sets for ToTTo were added to the GEM evaluation suite, 8 created specifically for the task and 1 coming from the original data. 1. We created subsets of the training and development sets of 500 randomly selected inputs each. 2. We applied input scrambling on a subset of 500 randomly selected test instances; the order of the highlighted cells was randomly reassigned. 3. For the input size, we created subpopulations based on the number of input highlighted cells in the whole table. \| Input length \| Frequency English \| \|---------------\|-------------------\| \| 1 \| 898 \| \| 2 \| 1850 \| \| 3 \| 2221 \| \| 4 \| 1369 \| \| 5 \| 483 \| \| 6 \| 379 \| \| 7 \| 124 \| \| 8 \| 128 \| \| 9 \| 61 \| \| 10 \| 40 \| \| 11 \| 20 \| \| 12 \| 26 \| \| 13 \| 10 \| \| 14 \| 14 \| \| 15 \| 14 \| \| 16 \| 7 \| \| 17 \| 6 \| \| 18 \| 5 \| \| 19 \| 5 \| \| 20 \| 5 \| \| 21 \| 4 \| \| 22 \| 1 \| \| 23 \| 2 \| \| 24 \| 4 \| \| 25 \| 1 \| \| 26...496 \| 1 \| 4. We also divided the test set according to the size of the whole table, based on the idea that larger tables represent a bigger space to take into account when generating the highlighted cells; a larger table could be more challenging to generate accurate text than a smaller table. There are 693 different table sizes, ranging from 2 to 15834 cells. \| Table size \|Frequency English\| \|-----------------\|-----------------\| \| 2 \| 71 \| \| 3 \| 52 \| \| 4 \| 36 \| \| 5 \| 41 \| \| 6 \| 144 \| \| 7 \| 47 \| \| 8 \| 59 \| \| 9 \| 105 \| \| 10 \| 162 \| \| 11 \| 36 \| \| 12 \| 158 \| \| 13 \| 35 \| \| 14 \| 79 \| \| 15 \| 136 \| \| 16 \| 111 \| \| 17 \| 48 \| \| 18 \| 123 \| \| 19 \| 29 \| \| 20 \| 112 \| \| 21 \| 91 \| \| 22 \| 17 \| \| 23 \| 7 \| \| 24 \| 169 \| \| 25 \| 56 \| \| 26 \| 12 \| \| 27 \| 40 \| \| 28 \| 77 \| \| 29 \| 7 \| \| 30 \| 122 \| \| 31 \| 4 \| \| 32 \| 49 \| \| 33 \| 21 \| \| 34 \| 7 \| \| 35 \| 103 \| \| 36 \| 131 \| \| 37 \| 10 \| \| 38 \| 6 \| \| 39 \| 26 \| \| 40 \| 110 \| \| 41 \| 1 \| \| 42 \| 54 \| \| 43 \| 6 \| \| 44 \| 47 \| \| 45 \| 79 \| \| 46 \| 4 \| \| 47 \| 2 \| \| 48 \| 114 \| \| 49 \| 18 \| \| 50 \| 55 \| \| 51 \| 11 \| \| 52 \| 43 \| \| 54 \| 80 \| \| 55 \| 73 \| \| 56 \| 64 \| \| 57 \| 12 \| \| 58 \| 1 \| \| 60 \| 114 \| \| 61 \| 4 \| \| 63 \| 39 \| \| 64 \| 36 \| \| 65 \| 62 \| \| 66 \| 48 \| \| 67 \| 1 \| \| 68 \| 36 \| \| 69 \| 6 \| \| 70 \| 81 \| \| 72 \| 76 \| \| 73 \| 1 \| \| 74 \| 1 \| \| 75 \| 44 \| \| 76 \| 33 \| \| 77 \| 30 \| \| 78 \| 66 \| \| 79 \| 1 \| \| 80 \| 83 \| \| 81 \| 12 \| \| 82 \| 1 \| \| 84 \| 80 \| \| 85 \| 25 \| \| 86 \| 1 \| \| 87 \| 3 \| \| 88 \| 35 \| \| 90 \| 78 \| \| 91 \| 18 \| \| 92 \| 22 \| \| 93 \| 5 \| \| 94 \| 2 \| \| 95 \| 31 \| \| 96 \| 50 \| \| 98 \| 11 \| \| 99 \| 14 \| \| 100 \| 48 \| \| 102 \| 24 \| \| 104 \| 29 \| \| 105 \| 36 \| \| 106 \| 2 \| \| 108 \| 51 \| \| 110 \| 31 \| \| ...8000+ \| (up to 10) \| 5. We also created three splits based on the subset of test examples in pages about people. We then used the structured information in WikiData to identify the following information: - gender (male, and female), - nationality grouped by continent (Africa, Asia, Europe, North America, Oceania, and South America) - ethnicity (African American and all USA) The categories within gender, ethnicity, and nationality were chosen based on data availability; The ToTTo dataset includes mostly tables that do not focus on people. As a result, only seven people in the original test set are marked as having a non-binary gender. Similar sparsity informed the grouping of nationalities by continent – only 19 countries are represented by more than 10 people in the test set. In case a person has citizenships across multiple continents, we may include the person in any of the included continents. Finally, ethnicity is very sparsely annotated in WikiData; only 150 test examples in ToTTo have this information and 128 of these are African Americans. We thus are unable to compare the performance on, e.g., Yoruba or Punjabi people, both of which have fewer than five instances. Another caveat here is that only 21 of the 128 people are female. We thus compare the African American population to results on a subset that includes all US citizens. #### Split Motivation <!-- info: What aspects of the model's generation capacities were the splits created to test? --> <!-- scope: periscope --> generalization, fairness, robustness ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> - The highest spot on the leaderboard is currently held by an anonymous method, with BLEU=49.2, PARENT=58.7 and BLEURT=0.249 on the _Overall_ test set. - The highest scoring non-anonymous method is the T5-based method of [Kale, 2020](https://arxiv.org/abs/2005.10433). This method uses a simple row-major linearization scheme to convert the table (it chooses only the highlighted cells and ignores the other cells - table titles and section titles are prefixed at the start of the respective section table) to a flat string. The linearized input - output description pairs from training examples are then used to finetune T5, with BLEU being used as the dev metric to pick checkpoints, and beam search with beam size 10 being the decoding method. Though the best numbers from this method are naturally from the largest T5-pretrained architecture (T5-3B), the paper shows improvements over the next-highest BERT-to-BERT method even when using T5-Base or T5-Small, which have the same and lesser parameters than BERT-to-BERT respectively. - The [Supplementary Repo](https://github.com/google-research/language/tree/master/language/totto) provides several useful modules to get started with for new approach implementation: 1. Code for the particular preprocessing / linearization scheme used to linearize the tables into flat sequences for the baseline approaches described in the paper has been described and shared [herein](https://github.com/google-research/language/tree/master/language/totto#baseline-preprocessing) 2. An [evaluation script](https://github.com/google-research/language/tree/master/language/totto#running-the-evaluation-scripts-locally) for locally scoring BLEU and PARENT system outputs on dev (or train) sets. Since BLEURT is a model-based metric, a [slightly separate](https://github.com/google-research/language/tree/master/language/totto#running-the-evaluation-scripts-locall://github.com/google-research/language/tree/master/language/totto#computing-the-bleurt-score) set of instructions is provided to evaluate on the same. ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Reasoning, surface realization #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `BLEU`, `BLEURT`, `Other: Other Metrics` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> Parent: a metric that measures the F-1 score of overlap between input content words and those used in references and those in generated text while ignoring the general surface form. It can thus measure the faithfulness much better than metrics that measure overlap with a reference #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> The metrics are used as in the leaderboard. The original paper additionally conducted a human evaluation focusing on fluency, faithfulness, and coverage. Faithfulness was measured as whether facts in the text are not supported by the input, and coverage as the number of highlighted cells that were considered. They thus represent precision and recall of the content. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> See leaderboard. ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> Tables occurring in Wikipedia articles were chosen as the data source with the following reasons in mind: 1. Wide coverage in terms of both vocabulary and concepts. 2. Wikipedia tables are not confined to a regular structure, with multi-row or multi-column cells occurring with a sufficient frequency. 3. Likely to contain reasonable-quality, natural text descriptions in the proximity of the table, which are also extractable by heuristics. (see the start of Section 4 for the heuristics used) To prevent an overlap with the earlier [Wikibio](https://arxiv.org/abs/1603.07771) dataset which focussed on Infobox-first sentence pairs from Wikipedia biography articles, the authors avoid using Infoboxes as a data source. The overall curation process of initially collecting free text and then annotator-revising it, was designed to combine the advantages of free-form text descriptions (which are fluent, high-quality and unhurriedly written, but also divergent and unfaithful) with annotator descriptions (which can be tailored to be faithful and to conform exactly to desired task requirements) #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> The speaker is required to produce a single, coherent English sentence that describes the highlighted cells in the given table, also using metadata and any other information from the table as applicable. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> wikipedia.org ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Crowdsourced` #### Where was it crowdsourced? <!-- info: If crowdsourced, where from? --> <!-- scope: periscope --> `Other crowdworker platform` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The basic source language producers are Wikipedia authors and/or editors, since the annotation starts with the natural text description near the Wikipedia table. The auxiliary source language producers are the annotators (two per example) who iteratively revise these descriptions to make them unambiguous and faithful to a subset of highlighted cells in the table. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by crowdworker #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> The initial table-description pairs are tables from Wikipedia articles, extracted through heuristics such as Number Matching (tables and sentences that overlap with a non-date number of atleast 3 non-zero digits) (Refer to Section 4 of the paper for more) 1. Table Readability: Tables which are deemed non-readable (due to foreign language, poor formatting etc - a very small fraction of 0.5%) are removed from the dataset here. 2. Cell Highlighting: The annotator highlights the cells of the table which support the description. 3. Deletion: The annotator removes phrases in the description which are not supported by the highlighted cells 4. Decontextualization: Descriptions may contain pronouns or other forms of anaphora, or other phenomena which depend on the overall article topic - these are fixed by replacement (e.g replacing pronouns with the entity, provided it occurs in the table). The replacements allowed are limited to one, and annotators are also instructed to conserve fluency. 5. Secondary Annotation: A second set of annotators is shown the output of Stage 4, and asked to fix it if required to ensure it is grammatical. The paper does not specifically describe the annotation platform or location profiles of the annotators. #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> algorithmically #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> After construction of the splits, the data curators filtered training examples that had rare table header combinations (<=5 examples) and which had an overlap with the validation or test splits. ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> yes #### Consent Policy Details <!-- info: What was the consent policy? --> <!-- scope: microscope --> Annotators were full time employees that were aware of the goal of the project and consented to having the data released as part of the dataset. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> no PII #### Justification for no PII <!-- info: Provide a justification for selecting `no PII` above. --> <!-- scope: periscope --> Since the source data is from wikipedia, only data in the public domain is included in the dataset. ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> yes #### Maintenance Plan Details <!-- info: Describe the original dataset's maintenance plan. --> <!-- scope: microscope --> For submissions, you can delete your data by emailing [email protected] from the email account used to sign up for the submission. Deletion requests will be responded to within 60 days. #### Maintainer Contact Information <!-- info: Provide contact information of a person responsible for the dataset maintenance --> <!-- scope: periscope --> Ankur Parikh ([email protected]) #### Any Contestation Mechanism? <!-- info: Does the maintenance plan include a contestation mechanism allowing individuals to request removal fo content? --> <!-- scope: periscope --> form submission #### Contestation Form Link <!-- info: Provide the form link or contact information --> <!-- scope: periscope --> [email protected] ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> yes #### Links and Summaries of Analysis Work <!-- info: Provide links to and summaries of works analyzing these biases. --> <!-- scope: microscope --> The original work as well as our GEM paper analyzes some biases #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> This dataset is created using tables and the table cell contents may hence naturally exhibit biases which have been found to exist in Wikipedia such as some forms of gender bias (e.g [(Graells-Garido et al.,2015)](https://labtomarket.files.wordpress.com/2018/01/wiki_gender_bias.pdf) notes that spouse information is more likely discussed for females than males) The table descriptions (targets/references) are, as discussed earlier, collected through a two-step process. 1. The natural text description near the table is taken as a starting point. This is Wikipedia article text as created upto that point in time by a chain of collaborative edits from Wikipedia authors. 2. The initial description is revised by chain of two or more annotated revisions, to make it unambiguous and faithful to a set of highlighted table cells. From their origin in 1), the descriptions may exhibit biases seen in Wikipedia text as mentioned above. From their revisions in 2), the descriptions may show biases originating from annotator-authored text, such as a preference for shorter descriptions since they're faster to write, or linguistic preferences influenced by the locations dominant in the annotator distribution. (However, note that these are likely to be much reduced since the annotators here are merely revising rather than completely authoring. Moreover, each sentence goes through atleast two annotators, which acts as a check against the personal biases of a single annotator.) Naturally-occurring text is also known to suffer from other biases such as reporting bias [(Gordon and Van Durme, 2013)](https://openreview.net/forum?id=AzxEzvpdE3Wcy&noteId=vmR8qaby8fqxittps://labtomarket.files.wordpress.com/2018/01/wiki_gender_bias.pdf) - this also applies to this dataset via its origin from Wikipedia. ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> Since the source data is from wikipedia, only data in the public domain is included in the dataset. ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `open license - commercial use allowed` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `open license - commercial use allowed` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The dataset is limited to topics that are present in Wikipedia, more specifically those topics that are present in articles which contain atleast one table _Sports_ and _Countries_ form 53.4% of the dataset. 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one-sec-cv12/chunk_192	one-sec-cv12	2023-04-11T20:07:15Z	297	0	[ "region:us" ]	null	2023-04-11T19:24:38Z	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 25040481984.5 num_examples: 260708 download_size: 21707886715 dataset_size: 25040481984.5 --- # Dataset Card for "chunk_192" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6925914287567139, -0.3622899055480957, 0.24782085418701172, 0.42177101969718933, -0.45763614773750305, -0.016253521665930748, 0.3202405571937561, -0.21230600774288177, 1.0547475814819336, 0.5618698000907898, -0.8221073150634766, -0.36450448632240295, -0.7043480277061462, -0.27279525995254517, -0.5994564294815063, 1.238311767578125, 0.11427118629217148, 0.044140610843896866, -0.31627756357192993, -0.0013066709507256746, -0.8169937133789062, -0.17498771846294403, -0.6577089428901672, -0.42120203375816345, 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the_pile_books3	null	2023-11-02T15:05:12Z	296	128	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:mit", "arxiv:2101.00027", "region:us" ]	[ "text-generation", "fill-mask" ]	2022-03-02T23:29:22Z	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - mit multilinguality: - monolingual pretty_name: Books3 size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling viewer: false dataset_info: features: - name: title dtype: string - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 108392037000 num_examples: 196639 download_size: 39516981435 dataset_size: 108392037000 --- # Dataset Card for the_pile_books3 ## Table of Contents - [Dataset Card for the_pile_books3](#dataset-card-for-the_pile_books3) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [\|split\|num examples\|](#splitnum-examples) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [GitHub](https://github.com/soskek/bookcorpus/issues/27#issuecomment-716104208) - Repository: [Needs More Information] - Paper: [arXiv](https://arxiv.org/abs/2101.00027) - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Defunct:</b> Dataset "the_pile_books3" is defunct and no longer accessible due to reported copyright infringement.</p> </div> This dataset is Shawn Presser's work and is part of EleutherAi/The Pile dataset. This dataset contains all of bibliotik in plain .txt form, aka 197,000 books processed in exactly the same way as did for bookcorpusopen (a.k.a. books1). seems to be similar to OpenAI's mysterious "books2" dataset referenced in their papers. Unfortunately OpenAI will not give details, so we know very little about any differences. People suspect it's "all of libgen", but it's purely conjecture. \|download_size\|36.8 Gib\| \|dataset_size\|100.9 Gib\| ### Supported Tasks and Leaderboards This dataset is used for Language Modeling. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances ``` {'title': '07 LEGO Ninjago - The Search For Zane (Scholastic) - Kate Howard (retail)' 'text': '\n\nTITLE PAGE\n\nFROM THE JOURNAL OF SENSEI GARMADON\n\nCHAPTER 1\n\nCHAPTER 2\n\nCHAPTER 3\n\nCHAPTER 4\n\nCHAPTER 5\n\nCHAPTER 6\n\nCHAPTER 7\n\nCHAPTER 8\n\nCHAPTER 9\n\nCOPYRIGHT\n\nThroughout Ninjago", five ninja are well-known for their speed, strength, and of course the elemental powers that help them protect our world from evil. But there are others who possess some of the same powers as the ninja. Others who may not always use their powers for good.\n\nBefore now, the ninja believed they were special. They di.......'} ``` ### Data Fields - `title`: title of the book - `text`: text content of the book ### Data Splits \|split\|num examples\| -------------------------------- \|train\|196640\| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information MIT ### Citation Information ``` @article{pile, title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` ### Contributions Thanks to [@shawwn](https://github.com/shawwn) for creating this dataset. Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.	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eth_py150_open	null	2022-11-18T20:01:17Z	295	0	[ "task_categories:other", "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:apache-2.0", "contextual-embeddings", "region:us" ]	[ "other" ]	2022-03-02T23:29:22Z	--- annotations_creators: - no-annotation language_creators: - machine-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: eth-py150-open pretty_name: ethpy150open tags: - contextual-embeddings dataset_info: features: - name: filepath dtype: string - name: license dtype: string config_name: eth_py150_open splits: - name: train num_bytes: 5414978 num_examples: 74749 - name: test num_bytes: 3006199 num_examples: 41457 - name: validation num_bytes: 598524 num_examples: 8302 download_size: 13875671 dataset_size: 9019701 --- # Dataset Card for ethpy150open ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.sri.inf.ethz.ch/py150 - Repository: https://github.com/google-research-datasets/eth_py150_open - Paper: https://proceedings.icml.cc/static/paper_files/icml/2020/5401-Paper.pdf - Leaderboard: None - Point of Contact: Aditya Kanade <[email protected]>, Petros Maniatis <[email protected]> ### Dataset Summary A redistributable subset of the [ETH Py150 corpus](https://www.sri.inf.ethz.ch/py150), introduced in the ICML 2020 paper ['Learning and Evaluating Contextual Embedding of Source Code'](https://proceedings.icml.cc/static/paper_files/icml/2020/5401-Paper.pdf) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure List of dicts of { "filepath": The relative URL containing the path to the file on GitHub "license": The license used for that specific file or repository } ### Data Instances { "filepath": "0rpc/zerorpc-python/setup.py", "license": "mit" }, { "filepath": "0rpc/zerorpc-python/zerorpc/heartbeat.py", "license": "mit" }, ### Data Fields - `filepath`: The relative URL containing the path to the file on GitHub - `license`: The license used for that specific file or repository ### Data Splits \| \| Train \| Valid \| Test \| \| ----- \| ------- \| ----- \| ----- \| \| Dataset Split \| 74749 \| 8302 \| 41457 \| ## Dataset Creation The original dataset is at https://www.sri.inf.ethz.ch/py150 ### Curation Rationale To generate a more redistributable version of the dataset ### Source Data #### Initial Data Collection and Normalization All the urls are filepaths relative to GitHub and the master branch was used as available at the time #### 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 Apache License 2.0 ### Citation Information @inproceedings{kanade2020learning, title={Learning and Evaluating Contextual Embedding of Source Code}, author={Kanade, Aditya and Maniatis, Petros and Balakrishnan, Gogul and Shi, Kensen}, booktitle={International Conference on Machine Learning}, pages={5110--5121}, year={2020}, organization={PMLR} } ### Contributions Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset.	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glucose	null	2022-11-18T20:04:16Z	295	2	[ "task_categories:fill-mask", "task_categories:text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended\|other-ROC-stories", "language:en", "license:cc-by-4.0", "commonsense-inference", "arxiv:2009.07758", "region:us" ]	[ "fill-mask", "text-generation" ]	2022-03-02T23:29:22Z	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-ROC-stories task_categories: - fill-mask - text-generation paperswithcode_id: glucose pretty_name: GLUCOSE tags: - commonsense-inference dataset_info: features: - name: experiment_id dtype: string - name: story_id dtype: string - name: worker_id dtype: int64 - name: worker_ids dtype: string - name: submission_time_normalized dtype: string - name: worker_quality_assessment dtype: int64 - name: selected_sentence_index dtype: int64 - name: story dtype: string - name: selected_sentence dtype: string - name: number_filled_in dtype: int64 - name: 1_specificNL dtype: string - name: 1_specificStructured dtype: string - name: 1_generalNL dtype: string - name: 1_generalStructured dtype: string - name: 2_specificNL dtype: string - name: 2_specificStructured dtype: string - name: 2_generalNL dtype: string - name: 2_generalStructured dtype: string - name: 3_specificNL dtype: string - name: 3_specificStructured dtype: string - name: 3_generalNL dtype: string - name: 3_generalStructured dtype: string - name: 4_specificNL dtype: string - name: 4_specificStructured dtype: string - name: 4_generalNL dtype: string - name: 4_generalStructured dtype: string - name: 5_specificNL dtype: string - name: 5_specificStructured dtype: string - name: 5_generalNL dtype: string - name: 5_generalStructured dtype: string - name: 6_specificNL dtype: string - name: 6_specificStructured dtype: string - name: 6_generalNL dtype: string - name: 6_generalStructured dtype: string - name: 7_specificNL dtype: string - name: 7_specificStructured dtype: string - name: 7_generalNL dtype: string - name: 7_generalStructured dtype: string - name: 8_specificNL dtype: string - name: 8_specificStructured dtype: string - name: 8_generalNL dtype: string - name: 8_generalStructured dtype: string - name: 9_specificNL dtype: string - name: 9_specificStructured dtype: string - name: 9_generalNL dtype: string - name: 9_generalStructured dtype: string - name: 10_specificNL dtype: string - name: 10_specificStructured dtype: string - name: 10_generalNL dtype: string - name: 10_generalStructured dtype: string config_name: glucose splits: - name: train num_bytes: 204605370 num_examples: 65522 - name: test num_bytes: 355757 num_examples: 500 download_size: 30362105 dataset_size: 204961127 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [Repository](https://github.com/TevenLeScao/glucose) - [Paper](https://arxiv.org/abs/2009.07758) - Point of Contact: [[email protected]](mailto:[email protected]) ### Dataset Summary GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning. Given a short story and a sentence X in the story, GLUCOSE captures ten dimensions of causal explanation related to X. These dimensions, inspired by human cognitive psychology, cover often-implicit causes and effects of X, including events, location, possession, and other attributes. ### Supported Tasks and Leaderboards Common sense inference of: 1. Causes 2. Emotions motivating an event 3. Locations enabling an event 4. Possession states enabling an event 5. Other attributes enabling an event 6. Consequences 7. Emotions caused by an event 8. Changes in location caused by an event 9. Changes in possession caused by an event 10. Other attributes that may be changed by an event ### Languages English, monolingual ## Dataset Structure ### Data Instances ``` { "experiment_id": "e56c7c3e-4660-40fb-80d0-052d566d676a__4", "story_id": "e56c7c3e-4660-40fb-80d0-052d566d676a", "worker_id": 19, "submission_time_normalized": "20190930", "worker_quality_rating": 3, "selected_sentence_index": 4, "story": "It was bedtime at our house. Two of the three kids hit the pillow and fall asleep. The third is a trouble maker. For two hours he continues to get out of bed and want to play. Finally he becomes tired and falls asleep." selected_sentence: "Finally he becomes tired and falls asleep.", "1_specificNL": "The third kid continues to get out of bed and wants to play >Causes/Enables> The kid finally becomes tired and falls asleep", "1_specificStructured": "{The third kid}_[subject] {continues}_[verb] {to }_[preposition1] {get out of bed}_[object1] {and wants to play}_[object2] >Causes/Enables> {The kid}_[subject] {finally becomes}_[verb] {tired}_[object1] {and falls asleep}_[object2]", "1_generalNL": "Someone_A doesn't want to go to sleep >Causes/Enables> Someone_A finally falls asleep", "1_generalStructured": "{Someone_A}_[subject] {doesn't want}_[verb] {to }_[preposition1] {go to sleep}_[object1] >Causes/Enables> {Someone_A}_[subject] {finally falls}_[verb] {asleep}_[object1]", "2_specificNL": "escaped", "2_specificStructured": "escaped", "2_generalNL": "escaped", "2_generalStructured": "escaped", "3_specificNL": "The third kid is in bed >Enables> The kid finally becomes tired and falls asleep", "3_specificStructured": "{The third kid}_[subject] {is}_[verb] {in}_[preposition] {bed}_[object] >Enables> {The kid}_[subject] {finally becomes}_[verb] {tired}_[object1] {and falls asleep}_[object2]", "3_generalNL": "Someone_A is in bed >Enables> Someone_A falls asleep", "3_generalStructured": "{Someone_A}_[subject] {is}_[verb] {in}_[preposition] {bed}_[object] >Enables> {Someone_A}_[subject] {falls}_[verb] {asleep}_[object1]", "4_specificNL": "escaped", "4_specificStructured": "escaped", "4_generalNL": "escaped", "4_generalStructured": "escaped", "5_specificNL": "escaped", "5_specificStructured": "escaped", "5_generalNL": "escaped", "5_generalStructured": "escaped", "6_specificNL": "escaped", "6_specificStructured": "escaped", "6_generalNL": "escaped", "6_generalStructured": "escaped", "7_specificNL": "escaped", "7_specificStructured": "escaped", "7_generalNL": "escaped", "7_generalStructured": "escaped", "8_specificNL": "escaped", "8_specificStructured": "escaped", "8_generalNL": "escaped", "8_generalStructured": "escaped", "9_specificNL": "escaped", "9_specificStructured": "escaped", "9_generalNL": "escaped", "9_generalStructured": "escaped", "10_specificNL": "escaped", "10_specificStructured": "escaped", "10_generalNL": "escaped", "10_generalStructured": "escaped", "number_filled_in": 7 } ``` ### Data Fields - __experiment_id__: a randomly generated alphanumeric sequence for a given story with the sentence index appended at the end after two underscores. Example: cbee2b5a-f2f9-4bca-9630-6825b1e36c13__0 - __story_id__: a random alphanumeric identifier for the story. Example: e56c7c3e-4660-40fb-80d0-052d566d676a - __worker_id__: each worker has a unique identificaiton number. Example: 21 - __submission_time_normalized__: the time of submission in the format YYYYMMDD. Example: 20200115 - __worker_quality_assessment__: rating for the worker on the assignment in the row. Example: 2 - __selected_sentence_index__: the index of a given sentence in a story. Example: 0 - __story__: contains the full text of the ROC story that was used for the HIT. Example: It was bedtime at our house. Two of the three kids hit the pillow and fall asleep. The third is a trouble maker. For two hours he continues to get out of bed and want to play. Finally he becomes tired and falls asleep. - __selected_sentence__: the sentence from the story that is being annotated. Example: It was bedtime at our house. - __[1-10]\_[specific/general][NL/Structured]__: This is the primary data collected. It provides the common sense knowledge about the related stories and those general rules about the world derived from the specific statements. For each of the ten relationships, there are four columns. The specific columns give the specific statements from the story. The general statements give the corresponding generalization. The NL columns are formatted in natural language, whereas the structured columns contain indications of the slots used to fill in the data. Example: - __1_specificNL__: "The school has a football team >Causes/Enables> The football game was last weekend" - __1_specificStructured__: "{The school }\_[subject] {has }\_[verb] {a football team }\_[object1] >Causes/Enables> {The football game }\_[subject] {was last weekend }\_[verb]" - __1_generalNL__: "Somewhere_A (that is a school ) has Something_A (that is a sports team ) >Causes/Enables> The game was last weekend" - __1_generalStructured__: "{Somewhere_A \|\|that is a school \|\|}\_[subject] {has }\_[verb] {Something_A \|\|that is a sports team \|\|}\_[object1] >Causes/Enables> {The game }\_[subject] {was last weekend }\_[verb]" - __number\_filled\_in__: number of dimensions filled in for the assignment. Example: 4 ### Data Splits Train split: 65,521 examples Test splits: 500 examples, without worker id and rating, number filled in, and structured text. ## Dataset Creation ### Curation Rationale When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. ### Source Data #### Initial Data Collection and Normalization Initial text from ROCStories #### Who are the source language producers? Amazon Mechanical Turk. ### Annotations #### Annotation process To enable developing models that can build mental models of narratives, we aimed to crowdsource a large, quality-monitored dataset. Beyond the scalability benefits, using crowd workers (as opposed to a small set of expert annotators) ensures diversity of thought, thus broadening coverage of a common-sense knowledge resource. The annotation task is complex: it requires annotators to understand different causal dimensions in a variety of contexts and to come up with generalized theories beyond the story context. For strict quality control, we designed a three-stage knowledge acquisition pipeline for crowdsourcing the GLUCOSE dataset on the Amazon Mechanical Turk Platform. The workers first go through a qualification test where they must score at least 90% on 10 multiple-choice questions on select GLUCOSE dimensions. Next, qualified workers can work on the main GLUCOSE data collection task: given a story S and a story sentence X, they are asked to fill in (allowing for non-applicable) all ten GLUCOSE dimensions, getting step-by-step guidance from the GLUCOSE data acquisition. To ensure data consistency, the same workers answer all dimensions for an S, X pair. Finally, the submissions are reviewed by an expert who rates each worker on a scale from 0 to 3, and provides feedback on how to improve. Our final UIs are the result of more than six rounds of pilot studies, iteratively improving the interaction elements, functionality, dimension definitions, instructions, and examples. #### Who are the annotators? Amazon Mechanical Turk workers, with feedback from an expert. ### Personal and Sensitive Information No personal or sensitive information. ## 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 Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll, from Elemental Cognition ### Licensing Information Creative Commons Attribution-NonCommercial 4.0 International Public License ### Citation Information ``` @inproceedings{mostafazadeh2020glucose, title={GLUCOSE: GeneraLized and COntextualized Story Explanations}, author={Nasrin Mostafazadeh and Aditya Kalyanpur and Lori Moon and David Buchanan and Lauren Berkowitz and Or Biran and Jennifer Chu-Carroll}, year={2020}, booktitle={The Conference on Empirical Methods in Natural Language Processing}, publisher={Association for Computational Linguistics} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset.	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hebrew_projectbenyehuda	null	2022-11-03T16:15:45Z	295	2	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:he", "license:mit", "region:us" ]	[ "text-generation", "fill-mask" ]	2022-03-02T23:29:22Z	--- annotations_creators: - expert-generated language_creators: - found language: - he license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Hebrew Projectbenyehuda dataset_info: features: - name: id dtype: int32 - name: url dtype: string - name: title dtype: string - name: authors dtype: string - name: translators dtype: string - name: original_language dtype: string - name: genre dtype: string - name: source_edition dtype: string - name: text dtype: string splits: - name: train num_bytes: 318732537 num_examples: 10078 download_size: 317749152 dataset_size: 318732537 --- # Dataset Card for Hebrew Projectbenyehuda ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/projectbenyehuda/public_domain_dump - Repository: https://github.com/projectbenyehuda/public_domain_dump - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This repository contains a dump of thousands of public domain works in Hebrew, from Project Ben-Yehuda, in plaintext UTF-8 files, with and without diacritics (nikkud), and in HTML files. The pseudocatalogue.csv file is a list of titles, authors, genres, and file paths, to help you process the dump. The Releases tab contains a downloadable ZIP archive of the full release. The git repo can be used to track individual file changes, or for incremenetal updates. In the ZIPs, each format (plaintext, plaintext stripped of diacritics, and HTML) has a ZIP file containing one directory per author, with all the author's works under that directory. To request changes or improvements to this dump, file an issue against this repository. All these works are in the public domain, so you are free to make any use of them, and do not need to ask for permission. If you would like to give credit, please credit "Project Ben-Yehuda volunteers", and include a link to the site. We'd also love to hear about the uses you've made of this dump, as it encourages us to keep producing the dump. E-mail us with a brief description (and links, if/as appropriate) of your re-use, at [email protected]. There are 10078 files, 3181136 lines Data Annotation: ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Hebrew ## Dataset Structure ### Data Instances Sample: ``` { 'id': 10, 'url': 'https://raw.githubusercontent.com/projectbenyehuda/public_domain_dump/master/txt/p23/m10.txt', 'title': 'חצי-נחמה', 'authors': 'אחד העם', 'translators': '', 'original_language': '', 'genre': 'מאמרים ומסות', 'source_edition': '', 'text': '\n\n\n\t\n\tחצי-נחמה\n\t\n\n\n\n1\n\nבין כל הצרות שנתחדשו עלינו בעת האחרונה תעשׂה ביחוד רושם מעציב בלב כל איש ישׂראל התחדשות ‘עלילת־הדם’. העלילה הנתעבה הזאת, בכל יָשנה, היתה ותהיה תמיד בעינינו כחדשה, ומימי הבינים ועד עתה תצטין בפעולתה החזקה על רוח עמנו, לא רק במקום המעשׂה, כי אם גם בארצות רחוקות שהגיעה אליהן השמועה.\n\nאמרתי: ‘על רוח עמנו’, כי אמנם רואה אני מקור החזיון הזה לא בסבּות חיצוניות, כי אם עמוק ברוח העם. בימי הבינים, שהיה כלל ישׂראל במקרים כאלה רגיל לחשוב עצמו כעומד במשפט ביחד עם אותם האומללים שעלה עליהם הגורל להיות כפּרותו, – יש מקום אמנם לראות בזה רק תוצאת הסכנה הגשמית הגדולה להכלל כולו, שהיתה כרוכה אז באמת בעקב כל עלילה כזו. גם לפני חמשים שנה, בימי מנוחה ושלוה, שעוררה עלילת דמשׂק רעש גדול כל־כך בארצות המערב, עדיין יש מקום לאמר, כי היתה בזה, להפך, יד הקנאה הגדולה לכבודם וזכויותיהם ששׂררה אז בלבות אחינו המערביים, אשר זה מעט יצאו מעבדות לחרות. אך בימינו אלה הרי מצד אחד אין הסכנה הגשמית גדולה עוד הרבה, ביחוד לקהלות רחוקות, ומצד אחר כבר הורגלנו לשמוע חרפתנו בקור רוח וקנאת כבודנו לא תאכלנו עוד, ואם בכל זאת גם עתה עודנו מתעוררים ומתנודדים בחזקה לשמע ‘עלילת־דם’, ורגש הכלל יתפרץ החוצה מכל עברים להשליך מעליו את החלאה הזאת, – אות הוא, כי לא הפחד ולא הכבוד החיצוני הם המניעים לזה, כי אם רוח העם הוא המרגיש פה את קלונו והוא זה המתעורר והמעורר; כי אעפ"י שבכל יתר הדברים כבר הביאונו צרותינו לאותו המצב שעליו אמר הנשׂיא החכם בימי קדם: ‘אין בשׂר המת מרגיש באיזמל’, – הנה פה אין ‘האיזמל’ חותך את ‘הבשׂר’ בלבד, כי אם עד הנפש יגע…\n\nאבל – ‘אין רע בלא טוב’, כלומר, בלא לקח טוב. גם הרע הגדול הזה שאנו עסוקים בו אינו ריק מלקח טוב, ואנחנו, אשר לא אדונים אנחנו לגורלנו וגם את הטוב גם את הרע נקבל מן החוץ שלא בטובתנו, ראוי לנו לבקש ברעותינו תמיד את התועלת הלמודית הצפונה בהן, והיתה לנו זאת, לפחות, חצי נחמה.\n\n\n\nאחד הכוחות היותר גדולים בחיי החברה הוא – ‘ההסכמה הכללית’. היו ימים שגם הפלוסופים ראו בהסכמה זו מופת נאמן על הדבר המוסכם ונתנו לה מקום בתוך שאר מופתיהם על מציאות האלהות. עתה אמנם יודעים הפלוסופים , שאין שקר ואין אולת אשר לא תוכל לבוא עליו ‘ההסכמה הכללית’, אם אך תנאי החיים נאותים לזה. אבל רק הפלוסופים יודעים זאת, ובעיני ההמון עוד גם עתה אין אַבטוֹריטט גדול מן ‘ההסכמה’: אם ‘כל העולם’ מאמינים שהדבר כן, בודאי כן הוא; ואם אני איני מבינו, אחרים מבינים; ואם אני רואה כעין סתירה לו, הרי ‘הכל’ רואים גם כן ואעפ"כ מאמינים, וכי חכם אני מכל העולם? – זה הוא בקירוב מהלך הרעיונות של האיש הפשוט, בדעת או בלי דעת ברורה, ומתוך כך הוא מסכים גם מצדו ונעשׂה בעצמו חלק מן ‘ההסכמה’.\n\nוכל־כך גדול כוח ‘ההסכמה’, עד שעל הרוב לא יוכל האדם למַלט נפשו מפעולתה גם כשהוא עצמו הוא ‘הדבר המוסכם’. אם ‘כל העולם’ אומרים על פלוני שגדול הוא בחכמה או ביראה, שיש בו מדה פלונית, טובה או רעה, – סופו להסכים לזה גם בעצמו, אע"פ שמתחלה לא מצא בנפשו אותו היתרון או החסרון שאחרים מיחסים לו. ולא זו בלבד אלא שההסכמה הזאת מצד ‘המוסכם’ עצמו פועלת מעט מעט על תכונת רוחו עד שמקרבתו באמת (או, לפחות, מולידה בו נטיה להתקרב) אל המצב ההוא שרואה בו ‘כל העולם’. על כן יזהירו הפדגוגים בצדק, לבלתי עורר את הילדים על מגרעותיהם המוסריות בראשית התפתחותן, וכל שכּן לבלתי יחס להם מגרעות שאין בהם, כי על ידי זה אפשר שנחזק בלבם את הראשונות ונוליד בם נטיה להאחרונות.\n\nואולם, הדבר מובן, כי ‘כל העולם’ אינו אחד לכל אחד. האדם רואה ‘עולמו’ רק באותה החברה שהוא חושב עצמו לחלק ממנה ורואה באישיה אנשים הקרובים לו מאיזה צד; אבל אין אדם חושב למאומה הסכמת אנשים שרוחם זרה לו לגמרי, שאינו מרגיש בנפשו שום יחס פנימי בינו ובינם. ככה אין האוֹרתוֹדוֹכּסים והמשׂכילים שלנו שׂמים לב כלל אלו להסכמתם של אלו, אף בדברים שאינם נוגעים לאמונה ודת, ושׂחקם ולעגם של אלו על אלו אינו עושׂה שום רושם בלבם של שניהם, לפי שכּל אחת משתי הכּתּות רואה את חברתה כאלו אינה. ואולם כשתנאי החיים מכריחים את בני הכתות השונות להמצא במשׂא ומתן תמידי זה עם זה והם מתרגלים לראות זה בזה קודם כל את האדם, – אז יתרחב ‘עולמם’ והשקפותיהם סובלות שנויים רבים על פי הסכמת ‘העולם’ במובנו החדש.\n\n\n\nלפיכך, בדורות שעברו, כשהיו אבותינו מאמינים בפשטו של ‘אתה בחרתנו’, לא היתה החרפּה שחרפום האומות פועלת כלל על טוהר נפשם פנימה. הם ידעו את ערכם ולא התפעלו עד מה מן ‘ההסכמה הכללית’ אשר מחוץ להם, בהיות כל חברת ‘המסכימים’ נחשבת בעיניהם למין מיוחד של בריות זרות להם ושונות מהם שנוי עצמי, בלי כל יחס וכל דמיון בינם ובינן. אז היה היהודי יכול לשמוע במנוחת לב כל המגרעות המוסריות והחטאים המעשׂיים שטפלה עליו הסכמת העמים, מבלי להרגיש בנפשו שום בושה או שפלוּת פנימית. כי מה לו ולמחשבות ‘הנכרים’ עליו ועל ערכּוֹ? לוּ רק יתנו לו לישב בשלוה! – אבל בדור הזה אין הדבר כן, עתה ‘עולמנו’ נתרחב הרבה, וההסכמה האירופּית פועלת עלינו בחזקה בכל ענפי החיים. ולפי שאין אנו מוציאים עוד את ‘הכל’ מן הכלל, לכן נתפעל בעל כרחנו ממה ש’הכל\' מוציאים אותנו מן הכלל, סופר אחד רוסי שאל באלו הימים בתמימוּת: אחר שכל העולם שׂונאים את היהודים, וכי אפשר לאמור, שכל העולם חייבים והיהודים זכאים? – ושאלה כזו מתגנבת עתה גם אל לב רבים מאחינו: וכי אפשר לאמור, שכל אותן התכונות הנשחתות והמעשׂים הרעים שכל העולם מיחס ליהודים אינם אלא ‘בדותא’?\n\nוהספק הזה, מכיון שנתעורר, מוצא לו מחיה בנקל באותם ההיקשים המוטעים ‘מן הפרט אל הכלל’ הרגילים מאד אצל המון בני האדם. הספור הידוע על דבר נוסע אחד, שבא לאחת הערים ונזדמן לאכסניא שהיה בה משרת כבד־פה, וכתב בפנקסו: בעיר פלונית משרתי האכסניות הם כבדי־פה, – הספור הזה מצייר בצורה של התוּל דרכי־ההגיון של ההמון ברוב משפטיו הכלליים. כל החזיונות הנראים באיזה דבר פרטי רגיל ההמון ליחס אל הכלל שהדבר ההוא מתחשב עליו לפי שמו התמידי, מבלי להתבונן, כי ‘פרט’ אחד יוכל להתחשב על ‘כללים’ רבים ביחד, כלומר, להיות שוּתף בתכוּנה אחת עם פרטיו של כלל אחד ובתכונה אחרת עם פרטיו של כלל אחר, בעוד שהשם הנקרא עליו מציין רק את התיחסותו לאחד הכללים באחד מצדדיו, לא בכולם. – על משפטים ממין זה תוכל להשען, וגם תשען באמת, ההסכמה הכללית ביחוסה אלינו: פלוני ופלוני הם יהודים לפי שמם ורמאים לפי תכוּנתם; שמע מינה, שהיהודים הם לפי תכונתם רמאים. ההגיון האמתי ישיב אמנם על זה, כי אף אם היו באמת כל היהודים בדורנו רמאים, אין מזה עוד ראיה, שהיהודים הם רמאים, כלומר, שתכוּנת הרמאוּת הנמצאת בכל יהודי נמצאת בו מצד התיחסותו אל הכלל ‘יהודים’ ולא מצד איזה כלל אחר (למשל, כלל ‘סוחרים’), שגם אליו מתיחס היהודי בתור פרט, ביחד עם אחרים אשר דבר אין להם עם הכלל ‘יהודים’. וכדי לברר הדבר, צריך לבדוֹק תחלה אותם ‘האחרים’ המשתתפים יחד עם היהודים בכללים אחרים. ורק אחר שנמצא על ידי בדיקה זו, שאין תכוּנת הרמאוּת מצויה בשום ‘כלל’ אחר המשותף ליהודים ולאחרים, – רק אז תהיה לנו צדקה לחרוץ משפט, כי היהדות היא אֵם הרמאוּת. – אבל, כאמור, אין דרכם של בני אדם להעמיק בהגיון, ואין אנו יכולים לדרוש כזאת גם מהמון בני עמנו. הם שומעים את המשפט החרוץ של ההסכמה הכללית ורואים עם זה, שרבים בקרבּנוּ כך הם באמת כמו שאומרת ההסכמה, ובזה די להם, והרי הם מתחילים להסכים גם בעצמם. וככה עוברות ‘תכוּנות היהודים’ כמטבע כשרה מיד ליד, מן ההסכמה החיצונית של העמים אל ההסכמה הפנימית בקרב עמנו, רק עם ההבדל הזה, שהעמים מונים את תכוּנותינו הרעות אחת לאחת בקול ענוֹת גבוּרה ולעג השאננים, ואנחנו עונים אחריהם מלה במלה בקול דממה דקה והצטדקות חלושה; הם ממשילים אותנו לכלי חרס, שאין לו תקנה אלא שבירה, ואנחנו ממשילים עצמנו לכלי מתכת, שאפשר לו בהגעלה ולבּוּן…\n\nהמצב הזה, אם יאריך ימים, יוכל לגרום לנו נזק מוסרי גדול. אין דבר מסוכּן לגוי ולאדם כהודאה על חטאים שאין בו. מי שחטא באמת, הרי שערי תשובה לא ננעלו, וברצונו הטוב יכול להסיר חלאתו מעליו. אבל מי שאחרים הביאוהו לחשוֹד עצמו במה שאין בו, איך יוכל להטהר בעיני עצמו? מצד אחד מאמין הוא לדברי האומרים לו: טול קורה מבין עיניך, ומצד אחר מרגיש הוא, שאינו יכול לטול את הקורה מבין עיניו, אחר שאינה באמת אלא בדמיון, והרי הוא במצב אותם המונומַנים הידועים, שמאיזו סבּה באו לידי אמונה, כי משׂא כבד תלוי להם בחוטמם מבלי שיוכלו להסירו. ולא עוד אלא שלפעמים תביא אמונה זו את האיש הפרטי להשתתף באותה המדה המגוּנה שלפי אמונתו היא קנין הכלל כולו, אעפ“י שהוא עצמו מצד פרטיותו אינו נוטה כלל לזה. אין ספק, למשל, כי בקרב העם שיצאו מתוכו אנשים כהרמב”ם נמצאים גם עתה בעלי דעה מיושבת ואוהבי סדר ושיטה בכל דבר, והם, בקחתם חלק בעבודת הצבּוּר, היו יכולים לתת בה את רוחם ולפעול גם על יתר העובדים. אבל מה נעשׂה, וכל גזרה ‘ההסכמה’, ששׂנאת הסדרים היא תכוּנה יהודית, וכבר הסכמנו גם אנחנו להסכמה זו (אעפ"י שעוד לא נתברר, אם התכוּנה הזאת, המצויה באמת בחלק גדול מעמנו, מתיחסת אל הכלל ‘יהודים’, או אולי – מה שיותר מתקבל על הלב – אל הכלל ‘חניכי־החדר’). ועל כן תרפינה ידי אוהבי הסדר, בהאמינם, כי אין עצה ואין תבונה נגד תכוּנת העם. ואם פטריוטים הם, יעקרו גם מלבם את האהבה לסדרים, המתנגדת לרוח עמם, ויעשׂו גם הם את מעשׂיהם כראוי ליהודים אמתיים…\n\n\n\nצריך איפוא לבקש איזה אמצעי, איך להוציא את עצמנו מתחת השפעת ‘ההסכמה הכללית’ בנוגע לתכוּנות ישׂראל וערכו המוסרי, כדי שלא נהיה בזויים בעיני עצמנו ולא נחשוב, שבאמת גרועים אנחנו מכל בני האדם תחת השמש, וכדי שלא נבוא עי"ז להיות ברבות הימים בפועל מה שאין אנו עתה אלא בדמיון.\n\nואת האמצעי הזה נותנת לנו ‘ההסכמה הכללית’ עצמה על ידי עלילת־הדם. העלילה הזאת היא היחידה בין כל רעותיה אשר בה לא תוכל ההסכמה להביא גם אותנו לידי ספק, אם באמת ‘כל העולם חייבים ואנחנו זכאים’, בהיותה מיוסדת כולה על שקר מוחלט ואין לה משען באיזה היקש מוטעה ‘מן הפרט על הכלל’. כל איש ישׂראל שנתחנך בתוך עמו יודע בבירור גמור, שאין בתוך כלל ישׂראל אף פרט אחד האוכל דם אדם לשם שמים. ואת הידיעה הברורה הזאת משגיאת ‘ההסכמה הכללית’, המתחדשת בלבנו מזמן לזמן על ידי התחדשות עלילת־הדם, צריכים אנו לשמור תמיד בזכרוננו, והיא תעזור לנו לעקור מלבנו את הנטיה להכּנע מפני האַבטוֹריטט של ‘כל העולם’ גם ביתר הדברים. יאמר כל העולם מה שיאמר על דבר פחיתוּת ערכּנוּ המוסרי, – אנחנו יודעים, כי ‘ההסכמה’ הזאת נשענת רק על הגיון המוני, בלי כל יסוד מדעי אמתּי. כי מי בא בסוד עמקי רוחנו וראה את ‘היהודי’ כמו שהוא מצד עצמו? מי שקל זה לעומת זה יהודים ושאינם יהודים הדומים אלו לאלו בכל יתר ‘הכללים’: סוחרים לעומת סוחרים, נרדפים לעומת נרדפים, רעבים לעומת רעבים וכו\'. – מי שקל כל אלה במאזני החכמה האמתּית ומצא את הכף מַכרעת לאחד הצדדים?\n\n‘וכי אפשר שכּל העולם חייבים והיהודים זכאים?’\n\nאפשר ואפשר, ועלילת־הדם תוכיח. פה הרי היהודים זכאים וטהורים כמלאכי השרת: יהודי ודם! היש שני הפכים גדולים מאלו? – ואף על פי כן…\n\n\n\nה\' תשרי תרנ"ג\n\n\n\n\n\n\nנדפס ב‘המליץ’ י“ד תשרי תרנ”ג. \xa0↩\n\n\n\n\n\n\n\n\n\n\nאת הטקסט לעיל הפיקו מתנדבי פרויקט בן־יהודה באינטרנט. הוא זמין תמיד בכתובת הבאה:https://benyehuda.org/read/10' } ``` ### Data Fields - `authors` - `genre` - `id` - `original_language` - `source_edition` - `text` - `title` - `translators` - `url` ### Data Splits \| \| train \| \|--------\|------:\| \| corpus \| 10078 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Researchers. ### 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 MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ### Citation Information ``` @article{, author = {}, title = {Public domain texts from Project Ben-Yehuda}, journal = {}, url = {https://github.com/projectbenyehuda/public_domain_dump}, year = {2020}, } ``` ### Contributions Thanks to [@imvladikon](https://github.com/imvladikon) for adding this dataset.	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opus_tedtalks	null	2022-11-03T16:15:24Z	295	0	[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:hr", "license:unknown", "region:us" ]	[ "translation" ]	2022-03-02T23:29:22Z	--- annotations_creators: - found language_creators: - found language: - en - hr license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: OpusTedtalks dataset_info: features: - name: id dtype: string - name: translation dtype: translation: languages: - en - hr config_name: en-hr splits: - name: train num_bytes: 15249417 num_examples: 86348 download_size: 5639306 dataset_size: 15249417 --- # Dataset Card for OpusTedtalks ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://opus.nlpl.eu/TedTalks.php - Repository: None - Paper: http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - Leaderboard: [More Information Needed] - Point of Contact: [More Information Needed] ### Dataset Summary This is a Croatian-English parallel corpus of transcribed and translated TED talks, originally extracted from https://wit3.fbk.eu. The corpus is compiled by Željko Agić and is taken from http://lt.ffzg.hr/zagic provided under the CC-BY-NC-SA license. This corpus is sentence aligned for both language pairs. The documents were collected and aligned using the Hunalign algorithm. ### 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC-BY-NC-SA license]<http://creativecommons.org/licenses/by-sa/3.0/> ### Citation Information @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.	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ASCCCCCCCC/amazon_zh	ASCCCCCCCC	2022-02-17T02:16:59Z	295	1	[ "license:apache-2.0", "region:us" ]	null	2022-03-02T23:29:22Z	--- license: apache-2.0 --- this is a datasets about amazon reviews	[ -0.39977237582206726, -0.12988899648189545, 0.0018812804482877254, 0.3322945833206177, -0.20463989675045013, 0.014537179842591286, 0.49392300844192505, -0.13317695260047913, 0.4707467257976532, 0.9639533758163452, -1.0071887969970703, -0.6702375411987305, -0.09026669710874557, 0.03024384193122387, -0.5813680291175842, 1.9163609743118286, 0.4269164800643921, 0.38267600536346436, 0.33176249265670776, -0.4801260232925415, 0.062241461127996445, -0.25822874903678894, -0.37362000346183777, 0.17368143796920776, 0.46330010890960693, 0.7602357268333435, 1.1116703748703003, -0.03919541835784912, 0.6103453040122986, 0.024125613272190094, 0.4272492229938507, -0.05443090200424194, -0.8046717047691345, 0.080722875893116, -0.1534445732831955, -0.47623521089553833, -0.28331610560417175, 0.15391576290130615, 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allenai/multi_lexsum	allenai	2023-05-18T21:41:22Z	295	13	[ "task_categories:summarization", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:odc-by", "arxiv:2206.10883", "region:us" ]	[ "summarization" ]	2022-08-03T15:51:10Z	--- annotations_creators: - expert-generated language: - en language_creators: - found license: - odc-by multilinguality: - monolingual pretty_name: Multi-LexSum size_categories: - 1K<n<10K - 10K<n<100K source_datasets: - original tags: [] task_categories: - summarization task_ids: [] --- # Dataset Card for Multi-LexSum ## Table of Contents - [Dataset Card for Multi-LexSum](#dataset-card-for-multi-lexsum) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset](#dataset) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Sheet (Datasheet)](#dataset-sheet-datasheet) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Release History](#release-history) ## Dataset Description - Homepage: https://multilexsum.github.io - Repository: https://github.com/multilexsum/dataset - Paper: https://arxiv.org/abs/2206.10883 <p> <a href="https://multilexsum.github.io" style="display: inline-block;"> <img src="https://img.shields.io/badge/-homepage-informational.svg?logo=jekyll" title="Multi-LexSum Paper" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a> <a href="https://github.com/multilexsum/dataset" style="display: inline-block;"> <img src="https://img.shields.io/badge/-multilexsum-lightgrey.svg?logo=github" title="Multi-LexSum Github Repo" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a> <a href="https://arxiv.org/abs/2206.10883" style="display: inline-block;"> <img src="https://img.shields.io/badge/NeurIPS-2022-9cf" title="Multi-LexSum is accepted in NeurIPS 2022" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a> </p> ### Talk @ NeurIPS 2022 [![Watch the video](https://img.youtube.com/vi/C-fwW_ZhkE8/0.jpg)](https://youtu.be/C-fwW_ZhkE8) ### Dataset Summary The Multi-LexSum dataset is a collection of 9,280 such legal case summaries. Multi-LexSum is distinct from other datasets in its multiple target summaries, each at a different granularity (ranging from one-sentence “extreme” summaries to multi-paragraph narrations of over five hundred words). It presents a challenging multi-document summarization task given the long length of the source documents, often exceeding two hundred pages per case. Unlike other summarization datasets that are (semi-)automatically curated, Multi-LexSum consists of expert-authored summaries: the experts—lawyers and law students—are trained to follow carefully created guidelines, and their work is reviewed by an additional expert to ensure quality. ### Languages English ## Dataset ### Data Fields The dataset contains a list of instances (cases); each instance contains the following data: \| Field \| Description \| \| ------------: \| -------------------------------------------------------------------------------: \| \| id \| `(str)` The case ID \| \| sources \| `(List[str])` A list of strings for the text extracted from the source documents \| \| summary/long \| `(str)` The long (multi-paragraph) summary for this case \| \| summary/short \| `(Optional[str])` The short (one-paragraph) summary for this case \| \| summary/tiny \| `(Optional[str])` The tiny (one-sentence) summary for this case \| Please check the exemplar usage below for loading the data: ```python from datasets import load_dataset multi_lexsum = load_dataset("allenai/multi_lexsum", name="v20230518") # Download multi_lexsum locally and load it as a Dataset object example = multi_lexsum["validation"][0] # The first instance of the dev set example["sources"] # A list of source document text for the case for sum_len in ["long", "short", "tiny"]: print(example["summary/" + sum_len]) # Summaries of three lengths print(example['case_metadata']) # The corresponding metadata for a case in a dict ``` ### Data Splits \| \| Instances \| Source Documents (D) \| Long Summaries (L) \| Short Summaries (S) \| Tiny Summaries (T) \| Total Summaries \| \| ----------: \| --------: \| -------------------: \| -----------------: \| ------------------: \| -----------------: \| --------------: \| \| Train (70%) \| 3,177 \| 28,557 \| 3,177 \| 2,210 \| 1,130 \| 6,517 \| \| Test (20%) \| 908 \| 7,428 \| 908 \| 616 \| 312 \| 1,836 \| \| Dev (10%) \| 454 \| 4,134 \| 454 \| 312 \| 161 \| 927 \| ## Dataset Sheet (Datasheet) Please check our [dataset sheet](https://multilexsum.github.io/datasheet) for details regarding dataset creation, source data, annotation, and considerations for the usage. ## Additional Information ### Dataset Curators The dataset is created by the collaboration between Civil Rights Litigation Clearinghouse (CRLC, from University of Michigan) and Allen Institute for AI. Multi-LexSum builds on the dataset used and posted by the Clearinghouse to inform the public about civil rights litigation. ### Licensing Information The Multi-LexSum dataset is distributed under the [Open Data Commons Attribution License (ODC-By)](https://opendatacommons.org/licenses/by/1-0/). The case summaries and metadata are licensed under the [Creative Commons Attribution License (CC BY-NC)](https://creativecommons.org/licenses/by-nc/4.0/), and the source documents are already in the public domain. Commercial users who desire a license for summaries and metadata can contact [[email protected]](mailto:[email protected]), which will allow free use but limit summary re-posting. The corresponding code for downloading and loading the dataset is licensed under the Apache License 2.0. ### Citation Information ``` @article{Shen2022MultiLexSum, author = {Zejiang Shen and Kyle Lo and Lauren Yu and Nathan Dahlberg and Margo Schlanger and Doug Downey}, title = {Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities}, journal = {CoRR}, volume = {abs/2206.10883}, year = {2022},**** url = {https://doi.org/10.48550/arXiv.2206.10883}, doi = {10.48550/arXiv.2206.10883} } ``` ## Release History \| Version \| Description \| \| ----------: \| -----------------------------------------------------------: \| \| `v20230518` \| The v1.1 release including case and source document metadata \| \| `v20220616` \| The initial v1.0 release \|	[ -0.434073805809021, -0.16245633363723755, 0.40000563859939575, 0.03979050740599632, -0.2932046353816986, 0.06858965009450912, -0.10367812216281891, -0.18904797732830048, 0.6000593304634094, 0.29379701614379883, 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vlsp-2023-vllm/ai2_arc_vi	vlsp-2023-vllm	2023-10-08T09:54:04Z	295	0	[ "region:us" ]	null	2023-09-29T18:17:01Z	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices struct: - name: label sequence: string - name: text sequence: string - name: answerKey dtype: string splits: - name: train num_bytes: 462541 num_examples: 1118 - name: validation num_bytes: 128948 num_examples: 298 - name: test num_bytes: 491761 num_examples: 1170 download_size: 511280 dataset_size: 1083250 --- Reference: https://huggingface.co/datasets/ai2_arc # ARC-Challenge (Vietnamese translation version) ## Dataset Summary A dataset of grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. ## Install To install `lm-eval` from the github repository main branch, run: ```bash git clone https://github.com/hieunguyen1053/lm-evaluation-harness cd lm-evaluation-harness pip install -e . ``` ## Basic Usage > Note: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info. ### Hugging Face `transformers` To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. vlsp-2023-vllm/hoa-1b4) on `ai2_arc_vi` you can use the following command: ```bash python main.py \ --model hf-causal \ --model_args pretrained=vlsp-2023-vllm/hoa-1b4 \ --tasks ai2_arc_vi \ --num_fewshot 25 \ --batch_size auto \ --device cuda:0 ``` Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model: ```bash python main.py \ --model hf-causal \ --model_args pretrained=vlsp-2023-vllm/hoa-1b4,revision=step100000,dtype="float" \ --tasks ai2_arc_vi \ --num_fewshot 25 \ --batch_size auto \ --device cuda:0 ``` To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you instead use `hf-seq2seq`. To evaluate (causal) models across multiple GPUs, use `--model hf-causal-experimental` > Warning: Choosing the wrong model may result in erroneous outputs despite not erroring.	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notrichardren/azaria-mitchell-diff-filtered	notrichardren	2023-10-03T04:33:20Z	295	0	[ "region:us" ]	null	2023-10-03T03:54:39Z	--- configs: - config_name: default data_files: - split: cities path: data/cities-* - split: companies path: data/companies-* - split: animals path: data/animals-* - split: elements path: data/elements-* - split: inventions path: data/inventions-* - split: facts path: data/facts-* dataset_info: features: - name: claim dtype: string - name: label dtype: int64 - name: dataset dtype: string - name: qa_type dtype: int64 - name: ind dtype: int64 splits: - name: cities num_bytes: 7955 num_examples: 112 - name: companies num_bytes: 14588 num_examples: 129 - name: animals num_bytes: 11451 num_examples: 137 - name: elements num_bytes: 11617 num_examples: 139 - name: inventions num_bytes: 10559 num_examples: 127 - name: facts num_bytes: 14809 num_examples: 159 download_size: 44699 dataset_size: 70979 --- # Dataset Card for "azaria-mitchell-diff-filtered" [More 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bigheiniuJ/CoTTrain-CoTCollection	bigheiniuJ	2023-11-17T04:59:52Z	295	0	[ "region:us" ]	null	2023-11-17T04:07:30Z	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: rationale dtype: string - name: task dtype: string - name: type dtype: string - name: output dtype: string - name: input dtype: string splits: - name: train num_bytes: 861688211 num_examples: 384272 download_size: 522795521 dataset_size: 861688211 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "CoTTrain-CoTCollection" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.689200222492218, -0.004939921200275421, 0.16724924743175507, 0.2549748420715332, -0.26903146505355835, 0.27527913451194763, 0.08641531318426132, -0.220447838306427, 0.7014307975769043, 0.483203649520874, -0.8731017112731934, -0.8663082718849182, -0.698535680770874, 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