Split (1)

train · 69.3k rows

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open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf	2023-09-18T06:46:57.000Z	[ "region:us" ]	open-llm-leaderboard	null	null	null	0	781	--- pretty_name: Evaluation run of meta-llama/Llama-2-70b-hf dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 124 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 10 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_meta-llama__Llama-2-70b-hf\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-18T06:46:44.905361](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf/blob/main/results_2023-09-18T06-46-44.905361.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\": 0.0017827181208053692,\n\ \ \"em_stderr\": 0.00043200973460388544,\n \"f1\": 0.06615562080536916,\n\ \ \"f1_stderr\": 0.0013739852117668813,\n \"acc\": 0.5885312292623206,\n\ \ \"acc_stderr\": 0.011707750309504293\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.00043200973460388544,\n\ \ \"f1\": 0.06615562080536916,\n \"f1_stderr\": 0.0013739852117668813\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.33965125094768767,\n \ \ \"acc_stderr\": 0.01304504506766526\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8374112075769534,\n \"acc_stderr\": 0.010370455551343326\n\ \ }\n}\n```" repo_url: https://huggingface.co/meta-llama/Llama-2-70b-hf leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|arc:challenge\|25_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|arc:challenge\|25_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|arc:challenge\|25_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|arc:challenge\|25_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-22T13:47:53.141854.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_08T23_38_08.931556 path: - '/details_harness\|drop\|3_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '/details_harness\|drop\|3_2023-09-18T06-46-44.905361.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-18T06-46-44.905361.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_08T23_38_08.931556 path: - '/details_harness\|gsm8k\|5_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '/details_harness\|gsm8k\|5_2023-09-18T06-46-44.905361.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-18T06-46-44.905361.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hellaswag\|10_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hellaswag\|10_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hellaswag\|10_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hellaswag\|10_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_0 data_files: - split: 2023_08_21T11_06_07.240233 path: - '/details_harness\|hendrycksTest-abstract_algebra\|0_2023-08-21T11:06:07.240233.parquet' - split: 2023_08_21T11_28_25.684618 path: - '/details_harness\|hendrycksTest-abstract_algebra\|0_2023-08-21T11:28:25.684618.parquet' - split: 2023_08_21T20_33_55.417483 path: - '/details_harness\|hendrycksTest-abstract_algebra\|0_2023-08-21T20:33:55.417483.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|0_2023-08-21T20:33:55.417483.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T09:05:23.035851.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T10:47:05.866748.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T13:42:09.433095.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T13:47:53.141854.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_abstract_algebra_0 data_files: - split: 2023_08_21T11_06_07.240233 path: - '/details_harness\|hendrycksTest-abstract_algebra\|0_2023-08-21T11:06:07.240233.parquet' - split: 2023_08_21T11_28_25.684618 path: - '/details_harness\|hendrycksTest-abstract_algebra\|0_2023-08-21T11:28:25.684618.parquet' - split: 2023_08_21T20_33_55.417483 path: - '/details_harness\|hendrycksTest-abstract_algebra\|0_2023-08-21T20:33:55.417483.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|0_2023-08-21T20:33:55.417483.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T13:47:53.141854.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_22T09_05_23.035851 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-22T09:05:23.035851.parquet' - split: 2023_08_22T10_47_05.866748 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-22T10:47:05.866748.parquet' - split: 2023_08_22T13_42_09.433095 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-22T13:42:09.433095.parquet' - split: 2023_08_22T13_47_53.141854 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-22T13:47:53.141854.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-22T13:47:53.141854.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_08T23_38_08.931556 path: - '/details_harness\|winogrande\|5_2023-09-08T23-38-08.931556.parquet' - split: 2023_09_18T06_46_44.905361 path: - '/details_harness\|winogrande\|5_2023-09-18T06-46-44.905361.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-18T06-46-44.905361.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:abstract_algebra\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:anatomy\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:astronomy\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:business_ethics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:clinical_knowledge\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_biology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_chemistry\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_computer_science\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_medicine\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_physics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:computer_security\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:conceptual_physics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:econometrics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:electrical_engineering\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:elementary_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:formal_logic\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:global_facts\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_biology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_chemistry\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_computer_science\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_european_history\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_geography\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_government_and_politics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_macroeconomics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_microeconomics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_physics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_psychology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_statistics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_us_history\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_world_history\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:human_aging\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:human_sexuality\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:international_law\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:jurisprudence\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:logical_fallacies\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:machine_learning\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:management\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:marketing\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:medical_genetics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:miscellaneous\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:moral_disputes\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:moral_scenarios\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:nutrition\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:philosophy\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:prehistory\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:professional_accounting\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:professional_law\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:professional_medicine\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:professional_psychology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:public_relations\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:security_studies\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:sociology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:us_foreign_policy\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:virology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:world_religions\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:abstract_algebra\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:anatomy\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:astronomy\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:business_ethics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:clinical_knowledge\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_biology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_chemistry\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_computer_science\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_medicine\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:college_physics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:computer_security\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:conceptual_physics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:econometrics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:electrical_engineering\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:elementary_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:formal_logic\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:global_facts\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_biology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_chemistry\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_computer_science\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_european_history\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_geography\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_government_and_politics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_macroeconomics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_microeconomics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_physics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_psychology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_statistics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_us_history\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:high_school_world_history\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:human_aging\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:human_sexuality\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:international_law\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:jurisprudence\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:logical_fallacies\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:machine_learning\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:management\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:marketing\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:medical_genetics\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:miscellaneous\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:moral_disputes\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:moral_scenarios\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:nutrition\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:philosophy\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:prehistory\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:professional_accounting\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:professional_law\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:professional_medicine\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:professional_psychology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:public_relations\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:security_studies\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:sociology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:us_foreign_policy\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:virology\|5_2023-08-28T20:36:26.123850.parquet' - '/details_original\|mmlu:world_religions\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:abstract_algebra\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:abstract_algebra\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:anatomy\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:anatomy\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:astronomy\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:astronomy\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:business_ethics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:business_ethics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:clinical_knowledge\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:clinical_knowledge\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:college_biology\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:college_biology\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:college_chemistry\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:college_chemistry\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:college_computer_science\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:college_computer_science\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:college_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:college_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:college_medicine\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:college_medicine\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:college_physics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:college_physics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:computer_security\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:computer_security\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:conceptual_physics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:conceptual_physics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:econometrics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:econometrics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:electrical_engineering\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:electrical_engineering\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:elementary_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:elementary_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:formal_logic\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:formal_logic\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:global_facts\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:global_facts\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_biology\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_biology\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_chemistry\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_chemistry\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_computer_science\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_computer_science\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_european_history\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_european_history\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_geography\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_geography\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_government_and_politics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_government_and_politics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_macroeconomics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_macroeconomics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_mathematics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_microeconomics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_microeconomics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_physics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_physics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_psychology\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_psychology\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_statistics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_statistics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_us_history\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_us_history\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:high_school_world_history\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:high_school_world_history\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:human_aging\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:human_aging\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:human_sexuality\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:human_sexuality\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:international_law\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:international_law\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:jurisprudence\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:jurisprudence\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:logical_fallacies\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:logical_fallacies\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:machine_learning\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:machine_learning\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:management\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:management\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:marketing\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:marketing\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:medical_genetics\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:medical_genetics\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:miscellaneous\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:miscellaneous\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:moral_disputes\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:moral_disputes\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:moral_scenarios\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:moral_scenarios\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:nutrition\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:nutrition\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:philosophy\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:philosophy\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:prehistory\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:prehistory\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:professional_accounting\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:professional_accounting\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:professional_law\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:professional_law\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:professional_medicine\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:professional_medicine\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:professional_psychology\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:professional_psychology\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:public_relations\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:public_relations\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:security_studies\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:security_studies\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:sociology\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:sociology\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:us_foreign_policy\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:us_foreign_policy\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:virology\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:virology\|5_2023-08-28T20:36:26.123850.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_36_26.123850 path: - '/details_original\|mmlu:world_religions\|5_2023-08-28T20:36:26.123850.parquet' - split: latest path: - '/details_original\|mmlu:world_religions\|5_2023-08-28T20:36:26.123850.parquet' - config_name: results data_files: - split: 2023_08_21T11_06_07.240233 path: - results_2023-08-21T11:06:07.240233.parquet - split: 2023_08_21T11_28_25.684618 path: - results_2023-08-21T11:28:25.684618.parquet - split: 2023_08_21T20_33_55.417483 path: - results_2023-08-21T20:33:55.417483.parquet - split: 2023_08_22T09_05_23.035851 path: - results_2023-08-22T09:05:23.035851.parquet - split: 2023_08_22T10_47_05.866748 path: - results_2023-08-22T10:47:05.866748.parquet - split: 2023_08_22T13_42_09.433095 path: - results_2023-08-22T13:42:09.433095.parquet - split: 2023_08_22T13_47_53.141854 path: - results_2023-08-22T13:47:53.141854.parquet - split: 2023_08_28T20_36_26.123850 path: - results_2023-08-28T20:36:26.123850.parquet - split: 2023_09_08T23_38_08.931556 path: - results_2023-09-08T23-38-08.931556.parquet - split: 2023_09_18T06_46_44.905361 path: - results_2023-09-18T06-46-44.905361.parquet - split: latest path: - results_2023-09-18T06-46-44.905361.parquet --- # Dataset Card for Evaluation run of meta-llama/Llama-2-70b-hf ## Dataset Description - Homepage: - Repository: https://huggingface.co/meta-llama/Llama-2-70b-hf - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 124 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 10 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_meta-llama__Llama-2-70b-hf", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-18T06:46:44.905361](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf/blob/main/results_2023-09-18T06-46-44.905361.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": 0.0017827181208053692, "em_stderr": 0.00043200973460388544, "f1": 0.06615562080536916, "f1_stderr": 0.0013739852117668813, "acc": 0.5885312292623206, "acc_stderr": 0.011707750309504293 }, "harness\|drop\|3": { "em": 0.0017827181208053692, "em_stderr": 0.00043200973460388544, "f1": 0.06615562080536916, "f1_stderr": 0.0013739852117668813 }, "harness\|gsm8k\|5": { "acc": 0.33965125094768767, "acc_stderr": 0.01304504506766526 }, "harness\|winogrande\|5": { "acc": 0.8374112075769534, "acc_stderr": 0.010370455551343326 } } ``` ### 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]
result-kand2-sdxl-wuerst-karlo/323c0619	2023-09-15T06:43:16.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	779	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 236 num_examples: 10 download_size: 1424 dataset_size: 236 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "323c0619" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
clarin-pl/polemo2-official	2022-08-29T16:40:01.000Z	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:8K", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-4.0", "region:us" ]	clarin-pl	PolEmo 2.0: Corpus of Multi-Domain Consumer Reviews, evaluation data for article presented at CoNLL.	@inproceedings{kocon-etal-2019-multi, title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews", author = "Koco{\'n}, Jan and Mi{\l}kowski, Piotr and Za{\'s}ko-Zieli{\'n}ska, Monika", booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/K19-1092", doi = "10.18653/v1/K19-1092", pages = "980--991",}	null	4	778	--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: 'Polemo2' size_categories: - 8K - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # Polemo2 ## Description The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is annotated with four labels: positive, negative, neutral, or ambiguous. ## Tasks (input, output and metrics) The task is to predict the correct label of the review. Input ('text' column): sentence Output ('target' column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous) Domain: Online reviews Measurements: Accuracy, F1 Macro Example: Input: `Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach , brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych ilościach i nie smaczne . Nie polecam nikomu tego hotelu .` Input (translated by DeepL): `At the very entrance the hotel stinks . In the rooms there is mold on the walls , dirty carpet . The bathroom smells of chemicals , the hotel does not heat in the rooms are cold . The room furnishings are old , the faucet moves , the door to the balcony does not close . The food is in small quantities and not tasty . I would not recommend this hotel to anyone .` Output: `1` (negative) ## Data splits \| Subset \| Cardinality \| \|--------\|------------:\| \| train \| 6573 \| \| val \| 823 \| \| test \| 820 \| ## Class distribution \| Class \| train \| dev \| test \| \|:--------\|--------:\|-------------:\|-------:\| \| minus \| 0.3756 \| 0.3694 \| 0.4134 \| \| plus \| 0.2775 \| 0.2868 \| 0.2768 \| \| amb \| 0.1991 \| 0.1883 \| 0.1659 \| \| zero \| 0.1477 \| 0.1555 \| 0.1439 \| ## Citation ``` @inproceedings{kocon-etal-2019-multi, title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews", author = "Koco{\'n}, Jan and Mi{\l}kowski, Piotr and Za{\'s}ko-Zieli{\'n}ska, Monika", booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/K19-1092", doi = "10.18653/v1/K19-1092", pages = "980--991", abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).", } ``` ## License ``` Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) ``` ## Links [HuggingFace](https://huggingface.co/datasets/clarin-pl/polemo2-official) [Source](https://clarin-pl.eu/dspace/handle/11321/710) [Paper](https://aclanthology.org/K19-1092/) ## Examples ### Loading ```python from pprint import pprint from datasets import load_dataset dataset = load_dataset("clarin-pl/polemo2-official") pprint(dataset['train'][0]) # {'target': 1, # 'text': 'Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach ' # ', brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w ' # 'pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się ' # 'rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych ' # 'ilościach i nie smaczne . Nie polecam nikomu tego hotelu .'} ``` ### Evaluation ```python import random from pprint import pprint from datasets import load_dataset, load_metric dataset = load_dataset("clarin-pl/polemo2-official") references = dataset["test"]["target"] # generate random predictions predictions = [random.randrange(max(references) + 1) for _ in range(len(references))] acc = load_metric("accuracy") f1 = load_metric("f1") acc_score = acc.compute(predictions=predictions, references=references) f1_score = f1.compute(predictions=predictions, references=references, average='macro') pprint(acc_score) pprint(f1_score) # {'accuracy': 0.2475609756097561} # {'f1': 0.23747048177471738} ```
result-kand2-sdxl-wuerst-karlo/f0cdf5c4	2023-09-15T09:18:20.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	776	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 207 num_examples: 10 download_size: 1427 dataset_size: 207 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "f0cdf5c4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dkoterwa/kor-sts	2023-07-25T09:52:30.000Z	[ "license:cc-by-sa-4.0", "region:us" ]	dkoterwa	null	null	null	0	775	--- license: cc-by-sa-4.0 dataset_info: features: - name: id dtype: int64 - name: genre dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 1034815 num_examples: 5691 - name: valid num_bytes: 297254 num_examples: 1465 - name: test num_bytes: 247409 num_examples: 1376 download_size: 837346 dataset_size: 1579478 --- # Korean Semantic Textual Similarity (KorSTS) Dataset For a better dataset description, please visit this GitHub repository prepared by the authors of the article: [LINK](https://github.com/kakaobrain/kor-nlu-datasets) <br> <br> This dataset was prepared by converting tsv files from this repository. The idea was to share the dataset for broader audience. I am not an original author of it. <br> Because of the specifity of read_csv method from Pandas library, there are couple of observations, which had to be deleted because of the formatting (54 in train, 35 in valid, and 1 in test) Additionaly, None values have been removed from the dataset (5 from train, 1 from eval, and 3 from test) How to download ``` from datasets import load_dataset data = load_dataset("dkoterwa/kor-sts") ``` If you use this dataset for research, please cite this paper: ``` @article{ham2020kornli, title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding}, author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon}, journal={arXiv preprint arXiv:2004.03289}, year={2020} } ```
SetFit/mrpc	2022-02-28T13:18:30.000Z	[ "region:us" ]	SetFit	null	null	null	4	774	# Glue MRPC This dataset is a port of the official [`mrpc` dataset](https://huggingface.co/datasets/glue/viewer/mrpc/train) on the Hub. Note that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively. Also, the test split is not labeled; the label column values are always -1.
result-kand2-sdxl-wuerst-karlo/d6e12779	2023-09-15T09:41:14.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	774	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 208 num_examples: 10 download_size: 1403 dataset_size: 208 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "d6e12779" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/a350d62a	2023-09-15T11:08:21.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	774	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 179 num_examples: 10 download_size: 1365 dataset_size: 179 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "a350d62a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rewoo/planner_instruction_tuning_2k	2023-05-22T04:54:20.000Z	[ "license:mit", "region:us" ]	rewoo	null	null	null	15	771	--- license: mit --- Bootstrap 2k Planner finetuning dataset for ReWOO. It is a mixture of "correct" HotpotQA and TriviaQA task planning trajectories in ReWOO Framework.
result-kand2-sdxl-wuerst-karlo/e395fcfb	2023-09-15T15:42:19.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	771	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 152 num_examples: 10 download_size: 1308 dataset_size: 152 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "e395fcfb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Alanox/stanford-dogs	2023-09-08T13:51:01.000Z	[ "license:mit", "region:us" ]	Alanox	The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization.	null	null	1	769	--- pretty_name: "Stanford Dogs" license: "mit" task_category: "Classification" --- # Dataset This dataset is extracted from [Stanford Dogs Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/) # Load ```python import datasets dataset = datasets.load_dataset("Alanox/stanford-dogs", split="full") print(dataset) """ Dataset({ features: ['name', 'annotations', 'target', 'image'], num_rows: 20580 }) """ print(dataset.features) """ { 'name': Value(dtype='string', id=None), 'annotations': Array2D(shape=(None, 4), dtype='int32', id=None), # ["xmin", "ymin", "xmax", "ymax"] 'target': Value(dtype='string', id=None), 'image': Image(decode=True, id=None) } """ ``` This dataset was created by the scripts from [this github repo](https://github.com/AlanBlanchet/ClassezDesImagesAvecDesAlgorithmesDeDeeplearning) # Fixes - `n02105855_2933.jpg` was not a `.jpg`. Converted all images to `.jpg`
result-kand2-sdxl-wuerst-karlo/94daaaa5	2023-09-15T16:14:38.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	769	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 198 num_examples: 10 download_size: 1363 dataset_size: 198 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "94daaaa5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dahoas/hf_cot_gsm8k	2023-10-01T14:40:46.000Z	[ "region:us" ]	Dahoas	null	null	null	0	768	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 8663589 num_examples: 7217 - name: val num_bytes: 301562 num_examples: 256 - name: test num_bytes: 1610805 num_examples: 1319 download_size: 5575205 dataset_size: 10575956 --- # Dataset Card for "hf_cot_gsm8k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/e06f76e8	2023-09-15T18:17:10.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	766	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 169 num_examples: 10 download_size: 1323 dataset_size: 169 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "e06f76e8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tner/bionlp2004	2022-08-10T01:01:51.000Z	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:other", "region:us" ]	tner	[BioNLP2004 NER dataset](https://aclanthology.org/W04-1213.pdf)	@inproceedings{collier-kim-2004-introduction, title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}", author = "Collier, Nigel and Kim, Jin-Dong", booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})", month = aug # " 28th and 29th", year = "2004", address = "Geneva, Switzerland", publisher = "COLING", url = "https://aclanthology.org/W04-1213", pages = "73--78", }	null	2	764	--- language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: BioNLP2004 --- # Dataset Card for "tner/bionlp2004" ## Dataset Description - Repository: [T-NER](https://github.com/asahi417/tner) - Paper: [https://aclanthology.org/U15-1010.pdf](https://aclanthology.org/U15-1010.pdf) - Dataset: BioNLP2004 - Domain: Biochemical - Number of Entity: 5 ### Dataset Summary BioNLP2004 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. BioNLP2004 dataset contains training and test only, so we randomly sample a half size of test instances from the training set to create validation set. - Entity Types: `DNA`, `protein`, `cell_type`, `cell_line`, `RNA` ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { 'tags': [0, 0, 0, 0, 3, 0, 9, 10, 0, 0, 0, 0, 0, 7, 8, 0, 3, 0, 0, 9, 10, 10, 0, 0], 'tokens': ['In', 'the', 'presence', 'of', 'Epo', ',', 'c-myb', 'mRNA', 'declined', 'and', '20', '%', 'of', 'K562', 'cells', 'synthesized', 'Hb', 'regardless', 'of', 'antisense', 'myb', 'RNA', 'expression', '.'] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/fin/raw/main/dataset/label.json). ```python { "O": 0, "B-DNA": 1, "I-DNA": 2, "B-protein": 3, "I-protein": 4, "B-cell_type": 5, "I-cell_type": 6, "B-cell_line": 7, "I-cell_line": 8, "B-RNA": 9, "I-RNA": 10 } ``` ### Data Splits \| name \|train\|validation\|test\| \|---------\|----:\|---------:\|---:\| \|bionlp2004 \|16619 \| 1927\| 3856\| ### Citation Information ``` @inproceedings{collier-kim-2004-introduction, title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}", author = "Collier, Nigel and Kim, Jin-Dong", booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})", month = aug # " 28th and 29th", year = "2004", address = "Geneva, Switzerland", publisher = "COLING", url = "https://aclanthology.org/W04-1213", pages = "73--78", } ```
result-kand2-sdxl-wuerst-karlo/bbe01f48	2023-09-15T18:27:46.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	764	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 217 num_examples: 10 download_size: 1377 dataset_size: 217 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "bbe01f48" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cambridgeltl/vsr_zeroshot	2023-03-22T17:27:58.000Z	[ "task_categories:text-classification", "task_categories:question-answering", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0", "multimodal", "vision-and-language", "arxiv:2205.00363", "region:us" ]	cambridgeltl	null	null	null	1	763	--- license: cc-by-4.0 task_categories: - text-classification - question-answering language: - en tags: - multimodal - vision-and-language pretty_name: VSR (zeroshot) size_categories: - 1K<n<10K --- # VSR: Visual Spatial Reasoning This is the zero-shot set of VSR: Visual Spatial Reasoning (TACL 2023) [[paper]](https://arxiv.org/abs/2205.00363). ### Usage ```python from datasets import load_dataset data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"} dataset = load_dataset("cambridgeltl/vsr_zeroshot", data_files=data_files) ``` Note that the image files still need to be downloaded separately. See [`data/`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data) for details. Go to our [github repo](https://github.com/cambridgeltl/visual-spatial-reasoning) for more introductions. ### Citation If you find VSR useful: ```bibtex @article{Liu2022VisualSR, title={Visual Spatial Reasoning}, author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier}, journal={Transactions of the Association for Computational Linguistics}, year={2023}, } ```
C-MTEB/T2Retrieval	2023-07-28T10:11:06.000Z	[ "region:us" ]	C-MTEB	null	null	null	0	761	--- configs: - config_name: default data_files: - split: corpus path: data/corpus-* - split: queries path: data/queries-* dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 265607316 num_examples: 118605 - name: queries num_bytes: 1000130 num_examples: 22812 download_size: 157606535 dataset_size: 266607446 --- # Dataset Card for "T2Retrieval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
teven/enwiki_100k	2023-04-03T17:16:55.000Z	[ "region:us" ]	teven	null	null	null	1	755	--- dataset_info: features: - name: metadata dtype: string - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 2570893740 num_examples: 1000000 download_size: 1550572660 dataset_size: 2570893740 --- # Dataset Card for "enwiki_100k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
fantasyfish/laion-art	2023-06-30T08:55:13.000Z	[ "region:us" ]	fantasyfish	null	null	null	0	755	--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: aesthetic dtype: float64 splits: - name: train num_bytes: 11640624315.8 num_examples: 20072 - name: test num_bytes: 538961083.0 num_examples: 855 download_size: 12347056207 dataset_size: 12179585398.8 --- # Dataset Card for "laion-art" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mstz/heart_failure	2023-04-16T17:31:15.000Z	[ "task_categories:tabular-classification", "size_categories:n<1K", "language:en", "license:cc", "heart failure", "tabular_classification", "binary_classification", "UCI", "region:us" ]	mstz	null	null	null	2	754	--- language: - en tags: - heart failure - tabular_classification - binary_classification - UCI pretty_name: Heart failure size_categories: - n<1K task_categories: - tabular-classification configs: - death license: cc --- # Heart failure The [Heart failure dataset](https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data) from Kaggle. Predict patient death from earth failure given some personal medical data . # Configurations and tasks \| Configuration \| Task \| Description \| \|-------------------\|---------------------------\|-----------------------------------------------------------------\| \| death \| Binary classification \| Did the patient die? \| # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/heart_failure", "death")["train"] ``` # Features \|Feature \|Type \| \|----------------------------------------------------\|-----------\| \|`age` \|`int8` \| \|`has_anaemia` \|`int8` \| \|`creatinine_phosphokinase_concentration_in_blood` \|`float64` \| \|`has_diabetes` \|`int8` \| \|`heart_ejection_fraction` \|`float64` \| \|`has_high_blood_pressure` \|`int8` \| \|`platelets_concentration_in_blood` \|`float64` \| \|`serum_creatinine_concentration_in_blood` \|`float64` \| \|`serum_sodium_concentration_in_blood` \|`float64` \| \|`sex` \|`int8` \| \|`is_smoker` \|`int8` \| \|`days_in_study` \|`int64` \|
result-kand2-sdxl-wuerst-karlo/1d35978a	2023-09-16T01:30:03.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	754	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 163 num_examples: 10 download_size: 1301 dataset_size: 163 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "1d35978a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mteb/biosses-sts	2022-09-27T19:13:38.000Z	[ "language:en", "region:us" ]	mteb	null	null	null	0	752	--- language: - en ---
wdc/products-2017	2022-10-23T05:50:24.000Z	[ "task_categories:text-classification", "annotations_creators:weak supervision", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]	wdc	Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match") In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test set. We provide training and validation sets in four different sizes for four product categories. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites.	@inproceedings{primpeli2019wdc, title={The WDC training dataset and gold standard for large-scale product matching}, author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian}, booktitle={Companion Proceedings of The 2019 World Wide Web Conference}, pages={381--386}, year={2019} }	null	1	751	--- annotations_creators: - weak supervision - expert-generated language: - en language_bcp47: - en-US license: - unknown multilinguality: - monolingual pretty_name: products-2017 size_categories: - 1K<n<10K - 10K<n<100K source_datasets: - original task_categories: - text-classification - data-integration task_ids: - entity-matching - identity-resolution - product-matching paperswithcode_id: wdc-products --- # Dataset Card for [products-2017] ## 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) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [LSPCv2 Homepage](http://webdatacommons.org/largescaleproductcorpus/v2/index.html) - Point of Contact: [Ralph Peeters](mailto:ralph.peeters@uni-mannheim.de) ### Dataset Summary Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match") In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test set. We provide training and validation sets in four different sizes for four product categories. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. ### Supported Tasks and Leaderboards Entity Matching, Product Matching ### Languages English ## Dataset Structure ### Data Instances The data is structured as pairs of product offers with the corresponding match/non-match label. This is an example instance from the computers category: ``` {"pair_id":"581109#16637861","label":0,"id_left":581109,"category_left":"Computers_and_Accessories","cluster_id_left":1324529,"brand_left":"\"Gigabyte\"@en","title_left":" \"Gigabyte Radeon RX 480 G1 Gaming 4096MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr\| OcUK\"@en","description_left":"\"GV-RX480G1 GAMING-4GD, Core Clock: 1202MHz, Boost Clock: 1290MHz, Memory: 4096MB 7000MHz GDDR5, Stream Processors: 2304, Crossfire Ready, VR Ready, FreeSync Ready, 3 Years Warranty\"@en ","price_left":null,"specTableContent_left":null,"id_right":16637861,"category_right":"Computers_and_Accessories","cluster_id_right":107415,"brand_right":"\"Gigabyte\"@en","title_right":" \"Gigabyte Radeon RX 550 Gaming OC 2048MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr\| OcUK\"@en","description_right":"\"GV-RX550GAMING OC-2GD, Boost: 1219MHz, Memory: 2048MB 7000MHz GDDR5, Stream Processors: 512, DirectX 12 Support, 3 Years Warranty\"@en ","price_right":null,"specTableContent_right":null} ``` ### Data Fields - pair_id: unique identifier of a pair (string) - label: binary label, match or non-match (int) The following attributes are contained twice, once for the first and once for the second product offer - id: unique id of the product offer (int) - category: product category (string) - cluster_id: id of the product cluster from the original corpus this offer belongs to (int) - brand: brand of the product (string) - title: product title (string) - description: longer product description (string) - price: price of the product offer (string) - specTableContent: additional data found in specification tables on the webpage that contains the product offer (string) ### Data Splits - Computers - Test set - 1100 pairs - Small Train set - 2267 pairs - Small Validation set - 567 pairs - Medium Train set - 6475 pairs - Medium Validation set - 1619 pairs - Large Train set - 26687 pairs - Large Validation set - 6672 pairs - XLarge Train set - 54768 pairs - Xlarge Validation set - 13693 pairs - Cameras - Test set - 1100 pairs - Small Train set - 1508 pairs - Small Validation set - 378 pairs - Medium Train set - 4204 pairs - Medium Validation set - 1051 pairs - Large Train set - 16028 pairs - Large Validation set - 4008 pairs - XLarge Train set - 33821 pairs - Xlarge Validation set - 8456 pairs - Watches - Test set - 1100 pairs - Small Train set - 1804 pairs - Small Validation set - 451 pairs - Medium Train set - 5130 pairs - Medium Validation set - 1283 pairs - Large Train set - 21621 pairs - Large Validation set - 5406 pairs - XLarge Train set - 49255 pairs - Xlarge Validation set - 12314 pairs - Shoes - Test set - 1100 pairs - Small Train set - 1650 pairs - Small Validation set - 413 pairs - Medium Train set - 4644 pairs - Medium Validation set - 1161 pairs - Large Train set - 18391 pairs - Large Validation set - 4598 pairs - XLarge Train set - 33943 pairs - Xlarge Validation set - 8486 pairs ## Dataset Creation ### Annotations #### Annotation process - Training and Validation sets: distant supervision via shared schema.org product IDs - Test sets: Single expert annotator #### Who are the annotators? [Ralph Peeters](https://www.uni-mannheim.de/dws/people/researchers/phd-students/ralph-peeters/) ## Additional Information ### Citation Information ``` @inproceedings{primpeli2019wdc, title={The WDC training dataset and gold standard for large-scale product matching}, author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian}, booktitle={Companion Proceedings of The 2019 World Wide Web Conference}, pages={381--386}, year={2019} } ```
madao33/new-title-chinese	2022-07-01T06:26:15.000Z	[ "region:us" ]	madao33	null	null	null	1	751	Entry not found
BeIR/nfcorpus	2022-10-23T06:01:44.000Z	[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]	BeIR	null	null	null	0	745	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## 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 [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
C-MTEB/T2Retrieval-qrels	2023-07-28T10:11:11.000Z	[ "region:us" ]	C-MTEB	null	null	null	0	744	--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: qid dtype: string - name: pid dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 3133383 num_examples: 118932 download_size: 1146734 dataset_size: 3133383 --- # Dataset Card for "T2Retrieval-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/a48196ad	2023-09-16T10:13:26.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	744	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 155 num_examples: 10 download_size: 1306 dataset_size: 155 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "a48196ad" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/8e18a25b	2023-09-16T15:18:38.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	739	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 191 num_examples: 10 download_size: 1358 dataset_size: 191 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "8e18a25b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/a2d1bcf0	2023-09-16T15:16:31.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	738	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 220 num_examples: 10 download_size: 1379 dataset_size: 220 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "a2d1bcf0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aqua_rat	2022-11-18T18:20:44.000Z	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:1705.04146", "region:us" ]	null	A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question.	@InProceedings{ACL, title = {Program induction by rationale generation: Learning to solve and explain algebraic word problems}, authors={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil}, year={2017} }	null	7	734	--- annotations_creators: - crowdsourced language_creators: - crowdsourced - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: aqua-rat pretty_name: Algebra Question Answering with Rationales dataset_info: - config_name: raw features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: correct dtype: string splits: - name: train num_bytes: 42333259 num_examples: 97467 - name: test num_bytes: 116779 num_examples: 254 - name: validation num_bytes: 118636 num_examples: 254 download_size: 47833135 dataset_size: 42568674 - config_name: tokenized features: - name: question dtype: string - name: options sequence: string - name: rationale dtype: string - name: correct dtype: string splits: - name: train num_bytes: 46493843 num_examples: 97467 - name: test num_bytes: 126283 num_examples: 254 - name: validation num_bytes: 128873 num_examples: 254 download_size: 52003894 dataset_size: 46748999 --- # Dataset Card for AQUA-RAT ## 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/deepmind/AQuA](https://github.com/deepmind/AQuA) - Repository: [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA) - Paper: [https://arxiv.org/pdf/1705.04146.pdf](https://arxiv.org/pdf/1705.04146.pdf) ### Dataset Summary A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question. ### Supported Tasks and Leaderboards ### Languages en ## Dataset Structure ### Data Instances ``` { "question": "A grocery sells a bag of ice for $1.25, and makes 20% profit. If it sells 500 bags of ice, how much total profit does it make?", "options": ["A)125", "B)150", "C)225", "D)250", "E)275"], "rationale": "Profit per bag = 1.25 * 0.20 = 0.25\nTotal profit = 500 * 0.25 = 125\nAnswer is A.", "correct": "A" } ``` ### Data Fields - `question` : (str) A natural language definition of the problem to solve - `options` : (list(str)) 5 possible options (A, B, C, D and E), among which one is correct - `rationale` : (str) A natural language description of the solution to the problem - `correct` : (str) The correct option ### Data Splits \| \| Train \| Valid \| Test \| \| ----- \| ------ \| ----- \| ---- \| \| Examples \| 97467 \| 254 \| 254 \| ## 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 Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ### Citation Information ``` @article{ling2017program, title={Program induction by rationale generation: Learning to solve and explain algebraic word problems}, author={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil}, journal={ACL}, year={2017} } ``` ### Contributions Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset.
covost2	2022-11-18T19:46:56.000Z	[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:extended\|other-common-voice", "language:ar", "language:ca", "language:cy", "language:de", "language:es", "language:et", "language:fa", "language:fr", "language:id", "language:it", "language:ja", "language:lv", "language:mn", "language:nl", "language:pt", "language:ru", "language:sl", "language:sv", "language:ta", "language:tr", "language:zh", "license:cc-by-nc-4.0", "arxiv:2007.10310", "region:us" ]	null	CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. The dataset is created using Mozilla’s open source Common Voice database of crowdsourced voice recordings. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .mp3 format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import torchaudio def map_to_array(batch): speech_array, _ = torchaudio.load(batch["file"]) batch["speech"] = speech_array.numpy() return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```	@misc{wang2020covost, title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus}, author={Changhan Wang and Anne Wu and Juan Pino}, year={2020}, eprint={2007.10310}, archivePrefix={arXiv}, primaryClass={cs.CL}	null	6	734	--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - ar - ca - cy - de - es - et - fa - fr - id - it - ja - lv - mn - nl - pt - ru - sl - sv - ta - tr - zh language_bcp47: - sv-SE - zh-CN license: - cc-by-nc-4.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - extended\|other-common-voice task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: null pretty_name: CoVoST 2 dataset_info: - config_name: en_de features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 110716293 num_examples: 289430 - name: validation num_bytes: 5971731 num_examples: 15531 - name: test num_bytes: 5689684 num_examples: 15531 download_size: 25779505 dataset_size: 122377708 - config_name: en_tr features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 109474265 num_examples: 289430 - name: validation num_bytes: 5914622 num_examples: 15531 - name: test num_bytes: 5619271 num_examples: 15531 download_size: 23659131 dataset_size: 121008158 - config_name: en_fa features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 119490720 num_examples: 289430 - name: validation num_bytes: 6423535 num_examples: 15531 - name: test num_bytes: 6103617 num_examples: 15531 download_size: 26148420 dataset_size: 132017872 - config_name: en_sv-SE features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 108557530 num_examples: 289430 - name: validation num_bytes: 5845918 num_examples: 15531 - name: test num_bytes: 5580039 num_examples: 15531 download_size: 23671482 dataset_size: 119983487 - config_name: en_mn features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 123950136 num_examples: 289430 - name: validation num_bytes: 6693044 num_examples: 15531 - name: test num_bytes: 6293633 num_examples: 15531 download_size: 27527436 dataset_size: 136936813 - config_name: en_zh-CN features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 106490939 num_examples: 289430 - name: validation num_bytes: 5735331 num_examples: 15531 - name: test num_bytes: 5487808 num_examples: 15531 download_size: 24280932 dataset_size: 117714078 - config_name: en_cy features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 109317182 num_examples: 289430 - name: validation num_bytes: 5894579 num_examples: 15531 - name: test num_bytes: 5626428 num_examples: 15531 download_size: 24224499 dataset_size: 120838189 - config_name: en_ca features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 109922455 num_examples: 289430 - name: validation num_bytes: 5924345 num_examples: 15531 - name: test num_bytes: 5623227 num_examples: 15531 download_size: 24167201 dataset_size: 121470027 - config_name: en_sl features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 107987860 num_examples: 289430 - name: validation num_bytes: 5838299 num_examples: 15531 - name: test num_bytes: 5537805 num_examples: 15531 download_size: 23421999 dataset_size: 119363964 - config_name: en_et features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 107707024 num_examples: 289430 - name: validation num_bytes: 5810185 num_examples: 15531 - name: test num_bytes: 5543309 num_examples: 15531 download_size: 23223843 dataset_size: 119060518 - config_name: en_id features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 109456930 num_examples: 289430 - name: validation num_bytes: 5896953 num_examples: 15531 - name: test num_bytes: 5634939 num_examples: 15531 download_size: 22904065 dataset_size: 120988822 - config_name: en_ar features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 116732296 num_examples: 289430 - name: validation num_bytes: 6280190 num_examples: 15531 - name: test num_bytes: 5947069 num_examples: 15531 download_size: 25301304 dataset_size: 128959555 - config_name: en_ta features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 146318684 num_examples: 289430 - name: validation num_bytes: 7944020 num_examples: 15531 - name: test num_bytes: 7411400 num_examples: 15531 download_size: 30037790 dataset_size: 161674104 - config_name: en_lv features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 109532576 num_examples: 289430 - name: validation num_bytes: 5905197 num_examples: 15531 - name: test num_bytes: 5625189 num_examples: 15531 download_size: 24573927 dataset_size: 121062962 - config_name: en_ja features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 114741253 num_examples: 289430 - name: validation num_bytes: 6161930 num_examples: 15531 - name: test num_bytes: 5883608 num_examples: 15531 download_size: 26664247 dataset_size: 126786791 - config_name: fr_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 75792665 num_examples: 207374 - name: validation num_bytes: 5487082 num_examples: 14760 - name: test num_bytes: 5525498 num_examples: 14760 download_size: 7282129 dataset_size: 86805245 - config_name: de_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 47678171 num_examples: 127834 - name: validation num_bytes: 5106253 num_examples: 13511 - name: test num_bytes: 5066500 num_examples: 13511 download_size: 9926797 dataset_size: 57850924 - config_name: es_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 29152515 num_examples: 79015 - name: validation num_bytes: 4974593 num_examples: 13221 - name: test num_bytes: 4983920 num_examples: 13221 download_size: 3202080 dataset_size: 39111028 - config_name: ca_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 35902579 num_examples: 95854 - name: validation num_bytes: 4798435 num_examples: 12730 - name: test num_bytes: 4804941 num_examples: 12730 download_size: 5021926 dataset_size: 45505955 - config_name: it_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 11952709 num_examples: 31698 - name: validation num_bytes: 3393315 num_examples: 8940 - name: test num_bytes: 3412207 num_examples: 8951 download_size: 1691247 dataset_size: 18758231 - config_name: ru_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 5610194 num_examples: 12112 - name: validation num_bytes: 2819414 num_examples: 6110 - 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config_name: nl_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 2468140 num_examples: 7108 - name: validation num_bytes: 594458 num_examples: 1699 - name: test num_bytes: 594979 num_examples: 1699 download_size: 543795 dataset_size: 3657577 - config_name: tr_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 1391148 num_examples: 3966 - name: validation num_bytes: 566458 num_examples: 1624 - name: test num_bytes: 570760 num_examples: 1629 download_size: 280904 dataset_size: 2528366 - config_name: ar_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 743065 num_examples: 2283 - name: validation num_bytes: 575077 num_examples: 1758 - name: test num_bytes: 552356 num_examples: 1695 download_size: 109802 dataset_size: 1870498 - config_name: sv-SE_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 698800 num_examples: 2160 - name: validation num_bytes: 438319 num_examples: 1349 - name: test num_bytes: 517738 num_examples: 1595 download_size: 96161 dataset_size: 1654857 - config_name: lv_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 747290 num_examples: 2337 - name: validation num_bytes: 360941 num_examples: 1125 - name: test num_bytes: 519183 num_examples: 1629 download_size: 88836 dataset_size: 1627414 - config_name: sl_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 602420 num_examples: 1843 - name: validation num_bytes: 165977 num_examples: 509 - name: test num_bytes: 115414 num_examples: 360 download_size: 58445 dataset_size: 883811 - config_name: ta_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 534564 num_examples: 1358 - name: validation num_bytes: 150428 num_examples: 384 - name: test num_bytes: 303843 num_examples: 786 download_size: 55659 dataset_size: 988835 - config_name: ja_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 396334 num_examples: 1119 - name: validation num_bytes: 226054 num_examples: 635 - name: test num_bytes: 241310 num_examples: 684 download_size: 54666 dataset_size: 863698 - config_name: id_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 406989 num_examples: 1243 - name: validation num_bytes: 259134 num_examples: 792 - name: test num_bytes: 277053 num_examples: 844 download_size: 51755 dataset_size: 943176 - config_name: cy_en features: - name: client_id dtype: string - name: file dtype: string - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 432071 num_examples: 1241 - name: validation num_bytes: 236107 num_examples: 690 - name: test num_bytes: 236713 num_examples: 690 download_size: 875557 dataset_size: 904891 --- # Dataset Card for covost2 ## 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/facebookresearch/covost - Repository: https://github.com/facebookresearch/covost - Paper: https://arxiv.org/abs/2007.10310 - Leaderboard: [Needs More Information] - Point of Contact: Changhan Wang (changhan@fb.com), Juan Miguel Pino (juancarabina@fb.com), Jiatao Gu (jgu@fb.com) ### Dataset Summary CoVoST 2 is a large-scale multilingual speech translation corpus covering translations from 21 languages into English \ and from English into 15 languages. The dataset is created using Mozillas open-source Common Voice database of \ crowdsourced voice recordings. There are 2,900 hours of speech represented in the corpus. ### Supported Tasks and Leaderboards `speech-translation`: The dataset can be used for Speech-to-text translation (ST). The model is presented with an audio file in one language and asked to transcribe the audio file to written text in another language. The most common evaluation metric is the BLEU score. Examples can be found at https://github.com/pytorch/fairseq/blob/master/examples/speech_to_text/docs/covost_example.md . ### Languages The dataset contains the audio, transcriptions, and translations in the following languages, French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian, Chinese, Welsh, Catalan, Slovenian, Estonian, Indonesian, Arabic, Tamil, Portuguese, Latvian, and Japanese. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file`, its transcription, called `sentence`, and the translation in target language called `translation`. ``` {'client_id': 'd277a1f3904ae00b09b73122b87674e7c2c78e08120721f37b5577013ead08d1ea0c053ca5b5c2fb948df2c81f27179aef2c741057a17249205d251a8fe0e658', 'file': '/home/suraj/projects/fairseq_s2t/covst/dataset/en/clips/common_voice_en_18540003.mp3', 'audio': {'path': '/home/suraj/projects/fairseq_s2t/covst/dataset/en/clips/common_voice_en_18540003.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000}, 'id': 'common_voice_en_18540003', 'sentence': 'When water is scarce, avoid wasting it.', 'translation': 'Wenn Wasser knapp ist, verschwenden Sie es nicht.'} ``` ### Data Fields - file: A path to the downloaded audio file in .mp3 format. - 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 transcription of the audio file in source language. - translation: The transcription of the audio file in the target language. - id: unique id of the data sample. ### Data Splits \| config \| train \| validation \| test \| \|----------\|--------\|------------\|-------\| \| en_de \| 289430 \| 15531 \| 15531 \| \| en_tr \| 289430 \| 15531 \| 15531 \| \| en_fa \| 289430 \| 15531 \| 15531 \| \| en_sv-SE \| 289430 \| 15531 \| 15531 \| \| en_mn \| 289430 \| 15531 \| 15531 \| \| en_zh-CN \| 289430 \| 15531 \| 15531 \| \| en_cy \| 289430 \| 15531 \| 15531 \| \| en_ca \| 289430 \| 15531 \| 15531 \| \| en_sl \| 289430 \| 15531 \| 15531 \| \| en_et \| 289430 \| 15531 \| 15531 \| \| en_id \| 289430 \| 15531 \| 15531 \| \| en_ar \| 289430 \| 15531 \| 15531 \| \| en_ta \| 289430 \| 15531 \| 15531 \| \| en_lv \| 289430 \| 15531 \| 15531 \| \| en_ja \| 289430 \| 15531 \| 15531 \| \| fr_en \| 207374 \| 14760 \| 14760 \| \| de_en \| 127834 \| 13511 \| 13511 \| \| es_en \| 79015 \| 13221 \| 13221 \| \| ca_en \| 95854 \| 12730 \| 12730 \| \| it_en \| 31698 \| 8940 \| 8951 \| \| ru_en \| 12112 \| 6110 \| 6300 \| \| zh-CN_en \| 7085 \| 4843 \| 4898 \| \| pt_en \| 9158 \| 3318 \| 4023 \| \| fa_en \| 53949 \| 3445 \| 3445 \| \| et_en \| 1782 \| 1576 \| 1571 \| \| mn_en \| 2067 \| 1761 \| 1759 \| \| nl_en \| 7108 \| 1699 \| 1699 \| \| tr_en \| 3966 \| 1624 \| 1629 \| \| ar_en \| 2283 \| 1758 \| 1695 \| \| sv-SE_en \| 2160 \| 1349 \| 1595 \| \| lv_en \| 2337 \| 1125 \| 1629 \| \| sl_en \| 1843 \| 509 \| 360 \| \| ta_en \| 1358 \| 384 \| 786 \| \| ja_en \| 1119 \| 635 \| 684 \| \| id_en \| 1243 \| 792 \| 844 \| \| cy_en \| 1241 \| 690 \| 690 \| ## 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 The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## 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 [CC BY-NC 4.0](https://github.com/facebookresearch/covost/blob/main/LICENSE) ### Citation Information ``` @misc{wang2020covost, title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus}, author={Changhan Wang and Anne Wu and Juan Pino}, year={2020}, eprint={2007.10310}, archivePrefix={arXiv}, primaryClass={cs.CL} ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
health_fact	2023-01-25T14:32:02.000Z	[ "task_categories:text-classification", "task_ids:fact-checking", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "arxiv:2010.09926", "region:us" ]	null	PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label. The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise. It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels. NOTE: There are missing labels in the dataset and we have replaced them with -1.	@inproceedings{kotonya-toni-2020-explainable, title = "Explainable Automated Fact-Checking for Public Health Claims", author = "Kotonya, Neema and Toni, Francesca", 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://www.aclweb.org/anthology/2020.emnlp-main.623", pages = "7740--7754", }	null	14	734	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking - multi-class-classification paperswithcode_id: pubhealth pretty_name: PUBHEALTH dataset_info: features: - name: claim_id dtype: string - name: claim dtype: string - name: date_published dtype: string - name: explanation dtype: string - name: fact_checkers dtype: string - name: main_text dtype: string - name: sources dtype: string - name: label dtype: class_label: names: '0': 'false' '1': mixture '2': 'true' '3': unproven - name: subjects dtype: string splits: - name: train num_bytes: 53985377 num_examples: 9832 - name: test num_bytes: 6825221 num_examples: 1235 - name: validation num_bytes: 6653044 num_examples: 1225 download_size: 24892660 dataset_size: 67463642 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: claim: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for PUBHEALTH ## 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: [PUBHEALTH homepage](https://github.com/neemakot/Health-Fact-Checking) - Repository: [PUBHEALTH repository](https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md) - Paper: [Explainable Automated Fact-Checking for Public Health Claims"](https://arxiv.org/abs/2010.09926) - Point of Contact:[Neema Kotonya](mailto:nk2418@ic.ac.uk) ### Dataset Summary PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances The following is an example instance of the PUBHEALTH dataset: \| Field \| Example \| \| ----------------- \| -------------------------------------------------------------\| \| __claim__ \| Expired boxes of cake and pancake mix are dangerously toxic. \| \| __explanation__ \| What's True: Pancake and cake mixes that contain mold can cause life-threatening allergic reactions. What's False: Pancake and cake mixes that have passed their expiration dates are not inherently dangerous to ordinarily healthy people, and the yeast in packaged baking products does not "over time develops spores." \| \| __label__ \| mixture \| \| __author(s)__ \| David Mikkelson \| \| __date published__ \| April 19, 2006 \| \| __tags__ \| food, allergies, baking, cake \| \| __main_text__ \| In April 2006, the experience of a 14-year-old who had eaten pancakes made from a mix that had gone moldy was described in the popular newspaper column Dear Abby. The account has since been circulated widely on the Internet as scores of concerned homemakers ponder the safety of the pancake and other baking mixes lurking in their larders [...] \| \| __evidence sources__ \| [1] Bennett, Allan and Kim Collins. “An Unusual Case of Anaphylaxis: Mold in Pancake Mix.” American Journal of Forensic Medicine & Pathology. September 2001 (pp. 292-295). [2] Phillips, Jeanne. “Dear Abby.” 14 April 2006 [syndicated column]. \| ### Data Fields Mentioned above in data instances. ### Data Splits \| \| # Instances \| \|-----------\|-------------\| \| train.tsv \| 9832 \| \| dev.tsv \| 1221 \| \| test.tsv \| 1235 \| \| total \| 12288 \| ## Dataset Creation ### Curation Rationale The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise. It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels. ### Source Data #### Initial Data Collection and Normalization The dataset was retrieved from the following fact-checking, news reviews and news websites: \| URL \| Type \| \|-----------------------------------\|--------------------\| \| http://snopes.com/ \| fact-checking \| \| http://politifact.com/ \| fact-checking \| \| http://truthorfiction.com/ \| fact-checking \| \| https://www.factcheck.org/ \| fact-checking \| \| https://fullfact.org/ \| fact-checking \| \| https://apnews.com/ \| news \| \| https://uk.reuters.com/ \| news \| \| https://www.healthnewsreview.org/ \| health news review \| #### 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 Not to our knowledge, but if it is brought to our attention that we are mistaken we will make the appropriate corrections to the dataset. ## 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 was created by Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020. ### Licensing Information MIT License ### Citation Information ``` @inproceedings{kotonya-toni-2020-explainable, title = "Explainable Automated Fact-Checking for Public Health Claims", author = "Kotonya, Neema and Toni, Francesca", 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://www.aclweb.org/anthology/2020.emnlp-main.623", pages = "7740--7754", } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.
teknium/GPT4-LLM-Cleaned	2023-05-04T01:48:35.000Z	[ "region:us" ]	teknium	null	null	null	84	734	This is the GPT4-LLM dataset from : https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM It has been filtered of all OpenAI disclaimers and refusals. (Disclaimer: It may have removed some additional things besides just OAI disclaimers, as I used the followings script which is a bit more broad: https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered/blob/main/wizardlm_clean.py) There is a modified script of that in the repo that was used specifically for this.
marsyas/gtzan	2022-11-06T20:34:20.000Z	[ "region:us" ]	marsyas	GTZAN is a dataset for musical genre classification of audio signals. The dataset consists of 1,000 audio tracks, each of 30 seconds long. It contains 10 genres, each represented by 100 tracks. The tracks are all 22,050Hz Mono 16-bit audio files in WAV format. The genres are: blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, and rock.	@misc{tzanetakis_essl_cook_2001, author = "Tzanetakis, George and Essl, Georg and Cook, Perry", title = "Automatic Musical Genre Classification Of Audio Signals", url = "http://ismir2001.ismir.net/pdf/tzanetakis.pdf", publisher = "The International Society for Music Information Retrieval", year = "2001" }	null	5	732	--- pretty_name: GTZAN --- # Dataset Card for GTZAN ## Table of Contents - [Dataset Card for GTZAN](#dataset-card-for-gtzan) - [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) - [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: [http://marsyas.info/downloads/datasets.html](http://marsyas.info/downloads/datasets.html) - Paper: [http://ismir2001.ismir.net/pdf/tzanetakis.pdf](http://ismir2001.ismir.net/pdf/tzanetakis.pdf) - Point of Contact: ### Dataset Summary GTZAN is a dataset for musical genre classification of audio signals. The dataset consists of 1,000 audio tracks, each of 30 seconds long. It contains 10 genres, each represented by 100 tracks. The tracks are all 22,050Hz Mono 16-bit audio files in WAV format. The genres are: blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, and rock. ### Languages English ## Dataset Structure GTZAN is distributed as a single dataset without a predefined training and test split. The information below refers to the single `train` split that is assigned by default. ### Data Instances An example of GTZAN looks as follows: ```python { "file": "/path/to/cache/genres/blues/blues.00000.wav", "audio": { "path": "/path/to/cache/genres/blues/blues.00000.wav", "array": array( [ 0.00732422, 0.01660156, 0.00762939, ..., -0.05560303, -0.06106567, -0.06417847, ], dtype=float32, ), "sampling_rate": 22050, }, "genre": 0, } ``` ### Data Fields The types associated with each of the data fields is as follows: * `file`: a `string` feature. * `audio`: an `Audio` feature containing the `path` of the sound file, the decoded waveform in the `array` field, and the `sampling_rate`. * `genre`: a `ClassLabel` feature. ### 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 ``` @misc{tzanetakis_essl_cook_2001, author = "Tzanetakis, George and Essl, Georg and Cook, Perry", title = "Automatic Musical Genre Classification Of Audio Signals", url = "http://ismir2001.ismir.net/pdf/tzanetakis.pdf", publisher = "The International Society for Music Information Retrieval", year = "2001" } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.
result-kand2-sdxl-wuerst-karlo/fbc48c23	2023-09-16T20:33:59.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	732	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 169 num_examples: 10 download_size: 1322 dataset_size: 169 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "fbc48c23" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
svhn	2023-01-25T14:45:04.000Z	[ "task_categories:image-classification", "task_categories:object-detection", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:other", "region:us" ]	null	SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.	@article{netzer2011reading, title={Reading digits in natural images with unsupervised feature learning}, author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y}, year={2011} }	null	9	731	--- annotations_creators: - machine-generated - expert-generated language_creators: - machine-generated language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - image-classification - object-detection task_ids: [] paperswithcode_id: svhn pretty_name: Street View House Numbers dataset_info: - config_name: full_numbers features: - name: image dtype: image - name: digits sequence: - name: bbox sequence: int32 length: 4 - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 390404309 num_examples: 33402 - name: test num_bytes: 271503052 num_examples: 13068 - name: extra num_bytes: 1868720340 num_examples: 202353 download_size: 2636187279 dataset_size: 2530627701 - config_name: cropped_digits features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 128364360 num_examples: 73257 - name: test num_bytes: 44464040 num_examples: 26032 - name: extra num_bytes: 967853504 num_examples: 531131 download_size: 1575594780 dataset_size: 1140681904 --- # Dataset Card for Street View House Numbers ## 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: http://ufldl.stanford.edu/housenumbers - Repository: - Paper: [Reading Digits in Natural Images with Unsupervised Feature Learning](http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf) - Leaderboard: https://paperswithcode.com/sota/image-classification-on-svhn - Point of Contact: streetviewhousenumbers@gmail.com ### Dataset Summary SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images. The dataset comes in two formats: 1. Original images with character level bounding boxes. 2. MNIST-like 32-by-32 images centered around a single character (many of the images do contain some distractors at the sides). ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for digit detection. - `image-classification`: The dataset can be used to train a model for Image Classification where the task is to predict a correct digit on the image. The leaderboard for this task is available at: https://paperswithcode.com/sota/image-classification-on-svhn ### Languages English ## Dataset Structure ### Data Instances #### full_numbers The original, variable-resolution, color house-number images with character level bounding boxes. ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=98x48 at 0x259E3F01780>, 'digits': { 'bbox': [ [36, 7, 13, 32], [50, 7, 12, 32] ], 'label': [6, 9] } } ``` #### cropped_digits Character level ground truth in an MNIST-like format. All digits have been resized to a fixed resolution of 32-by-32 pixels. The original character bounding boxes are extended in the appropriate dimension to become square windows, so that resizing them to 32-by-32 pixels does not introduce aspect ratio distortions. Nevertheless this preprocessing introduces some distracting digits to the sides of the digit of interest. ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x25A89494780>, 'label': 1 } ``` ### Data Fields #### full_numbers - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `digits`: a dictionary containing digits' bounding boxes and labels - `bbox`: a list of bounding boxes (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) corresponding to the digits present on the image - `label`: a list of integers between 0 and 9 representing the digit. #### cropped_digits - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `digit`: an integer between 0 and 9 representing the digit. ### Data Splits #### full_numbers The data is split into training, test and extra set. The training set contains 33402 images, test set 13068 and the extra set 202353 images. #### cropped_digits The data is split into training, test and extra set. The training set contains 73257 images, test set 26032 and the extra set 531131 images. The extra set can be used as extra training data. The extra set was obtained in a similar manner to the training and test set, but with the increased detection threshold in order to generate this large amount of labeled data. The SVHN extra subset is thus somewhat biased toward less difficult detections, and is thus easier than SVHN train/SVHN test. ## Dataset Creation ### Curation Rationale From the paper: > As mentioned above, the venerable MNIST dataset has been a valuable goal post for researchers seeking to build better learning systems whose benchmark performance could be expected to translate into improved performance on realistic applications. However, computers have now reached essentially human levels of performance on this problem—a testament to progress in machine learning and computer vision. The Street View House Numbers (SVHN) digit database that we provide can be seen as similar in flavor to MNIST (e.g., the images are of small cropped characters), but the SVHN dataset incorporates an order of magnitude more labeled data and comes from a significantly harder, unsolved, real world problem. Here the gap between human performance and state of the art feature representations is significant. Going forward, we expect that this dataset may fulfill a similar role for modern feature learning algorithms: it provides a new and difficult benchmark where increased performance can be expected to translate into tangible gains on a realistic application. ### Source Data #### Initial Data Collection and Normalization From the paper: > The SVHN dataset was obtained from a large number of Street View images using a combination of automated algorithms and the Amazon Mechanical Turk (AMT) framework, which was used to localize and transcribe the single digits. We downloaded a very large set of images from urban areas in various countries. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process From the paper: > From these randomly selected images, the house-number patches were extracted using a dedicated sliding window house-numbers detector using a low threshold on the detector’s confidence score in order to get a varied, unbiased dataset of house-number signs. These low precision detections were screened and transcribed by AMT workers. #### Who are the annotators? The AMT workers. ### 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 Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu and Andrew Y. Ng ### Licensing Information Non-commerical use only. ### Citation Information ``` @article{netzer2011reading, title={Reading digits in natural images with unsupervised feature learning}, author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y}, year={2011} } ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
OxAISH-AL-LLM/wiki_toxic	2022-09-19T15:53:19.000Z	[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended\|other", "language:en", "license:cc0-1.0", "wikipedia", "toxicity", "toxic comments", "region:us" ]	OxAISH-AL-LLM	Jigsaw Toxic Comment Challenge dataset. This dataset was the basis of a Kaggle competition run by Jigsaw	""" _DESCRIPTION =	null	8	729	--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc0-1.0 multilinguality: - monolingual pretty_name: Toxic Wikipedia Comments size_categories: - 100K<n<1M source_datasets: - extended\|other tags: - wikipedia - toxicity - toxic comments task_categories: - text-classification task_ids: - hate-speech-detection --- # Dataset Card for Wiki Toxic ## 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: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The Wiki Toxic dataset is a modified, cleaned version of the dataset used in the [Kaggle Toxic Comment Classification challenge](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/overview) from 2017/18. The dataset contains comments collected from Wikipedia forums and classifies them into two categories, `toxic` and `non-toxic`. The Kaggle dataset was cleaned using the included `clean.py` file. ### Supported Tasks and Leaderboards - Text Classification: the dataset can be used for training a model to recognise toxicity in sentences and classify them accordingly. ### Languages The sole language used in the dataset is English. ## Dataset Structure ### Data Instances For each data point, there is an id, the comment_text itself, and a label (0 for non-toxic, 1 for toxic). ``` {'id': 'a123a58f610cffbc', 'comment_text': '"This article SUCKS. It may be poorly written, poorly formatted, or full of pointless crap that no one cares about, and probably all of the above. If it can be rewritten into something less horrible, please, for the love of God, do so, before the vacuum caused by its utter lack of quality drags the rest of Wikipedia down into a bottomless pit of mediocrity."', 'label': 1} ``` ### Data Fields - `id`: A unique identifier string for each comment - `comment_text`: A string containing the text of the comment - `label`: An integer, either 0 if the comment is non-toxic, or 1 if the comment is toxic ### Data Splits The Wiki Toxic dataset has three splits: train, validation, and test. The statistics for each split are below: \| Dataset Split \| Number of data points in split \| \| ----------- \| ----------- \| \| Train \| 127,656 \| \| Validation \| 31,915 \| \| Test \| 63,978 \| ## 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 [@github-username](https://github.com/<github-username>) for adding this dataset.
wiki_snippets	2023-04-05T13:43:20.000Z	[ "task_categories:text-generation", "task_categories:other", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:extended\|wiki40b", "source_datasets:extended\|wikipedia", "language:en", "license:unknown", "text-search", "region:us" ]	null	Wikipedia version split into plain text snippets for dense semantic indexing.	@ONLINE {wikidump, author = {Wikimedia Foundation}, title = {Wikimedia Downloads}, url = {https://dumps.wikimedia.org} }	null	0	728	--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - unknown multilinguality: - multilingual pretty_name: WikiSnippets size_categories: - 10M<n<100M source_datasets: - extended\|wiki40b - extended\|wikipedia task_categories: - text-generation - other task_ids: - language-modeling paperswithcode_id: null tags: - text-search dataset_info: - config_name: wiki40b_en_100_0 features: - name: _id dtype: string - name: datasets_id dtype: int32 - name: wiki_id dtype: string - name: start_paragraph dtype: int32 - name: start_character dtype: int32 - name: end_paragraph dtype: int32 - name: end_character dtype: int32 - name: article_title dtype: string - name: section_title dtype: string - name: passage_text dtype: string splits: - name: train num_bytes: 12938641686 num_examples: 17553713 download_size: 0 dataset_size: 12938641686 - config_name: wikipedia_en_100_0 features: - name: _id dtype: string - name: datasets_id dtype: int32 - name: wiki_id dtype: string - name: start_paragraph dtype: int32 - name: start_character dtype: int32 - name: end_paragraph dtype: int32 - name: end_character dtype: int32 - name: article_title dtype: string - name: section_title dtype: string - name: passage_text dtype: string splits: - name: train num_bytes: 26407884393 num_examples: 33849898 download_size: 0 dataset_size: 26407884393 --- # Dataset Card for "wiki_snippets" ## 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://dumps.wikimedia.org](https://dumps.wikimedia.org) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - 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) ### Dataset Summary Wikipedia version split into plain text snippets for dense semantic indexing. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure We show detailed information for 2 configurations of the dataset (with 100 snippet passage length and 0 overlap) in English: - wiki40b_en_100_0: Wiki-40B - wikipedia_en_100_0: Wikipedia ### Data Instances #### wiki40b_en_100_0 - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 12.94 GB - Total amount of disk used: 12.94 GB An example of 'train' looks as follows: ``` {'_id': '{"datasets_id": 0, "wiki_id": "Q1294448", "sp": 2, "sc": 0, "ep": 6, "ec": 610}', 'datasets_id': 0, 'wiki_id': 'Q1294448', 'start_paragraph': 2, 'start_character': 0, 'end_paragraph': 6, 'end_character': 610, 'article_title': 'Ági Szalóki', 'section_title': 'Life', 'passage_text': "Ági Szalóki Life She started singing as a toddler, considering Márta Sebestyén a role model. Her musical background is traditional folk music; she first won recognition for singing with Ökrös in a traditional folk style, and Besh o droM, a Balkan gypsy brass band. With these ensembles she toured around the world from the Montreal Jazz Festival, through Glastonbury Festival to the Théatre de la Ville in Paris, from New York to Beijing.\nSince 2005, she began to pursue her solo career and explore various genres, such as jazz, thirties ballads, or children's songs.\nUntil now, three of her six released albums"} ``` #### wikipedia_en_100_0 - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 26.41 GB - Total amount of disk used: 26.41 GB An example of 'train' looks as follows: ``` {'_id': '{"datasets_id": 0, "wiki_id": "Anarchism", "sp": 0, "sc": 0, "ep": 2, "ec": 129}', 'datasets_id': 0, 'wiki_id': 'Anarchism', 'start_paragraph': 0, 'start_character': 0, 'end_paragraph': 2, 'end_character': 129, 'article_title': 'Anarchism', 'section_title': 'Start', 'passage_text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement, and has a strong historical association with anti-capitalism and socialism. Humans lived in societies without formal hierarchies long before the establishment of formal states, realms, or empires. With the'} ``` ### Data Fields The data fields are the same for all configurations: - `_id`: a `string` feature. - `datasets_id`: a `int32` feature. - `wiki_id`: a `string` feature. - `start_paragraph`: a `int32` feature. - `start_character`: a `int32` feature. - `end_paragraph`: a `int32` feature. - `end_character`: a `int32` feature. - `article_title`: a `string` feature. - `section_title`: a `string` feature. - `passage_text`: a `string` feature. ### Data Splits \| name \| train \| \|:-------------------\|---------:\| \| wiki40b_en_100_0 \| 17553713 \| \| wikipedia_en_100_0 \| 33849898 \| ## 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 See licensing information of source datasets. ### Citation Information Cite source datasets: - Wiki-40B: ``` @inproceedings{49029, title = {Wiki-40B: Multilingual Language Model Dataset}, author = {Mandy Guo and Zihang Dai and Denny Vrandecic and Rami Al-Rfou}, year = {2020}, booktitle = {LREC 2020} } ``` - Wikipedia: ``` @ONLINE{wikidump, author = "Wikimedia Foundation", title = "Wikimedia Downloads", url = "https://dumps.wikimedia.org" } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@yjernite](https://github.com/yjernite) for adding this dataset.
result-kand2-sdxl-wuerst-karlo/0dc6521d	2023-09-16T23:57:15.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	727	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 166 num_examples: 10 download_size: 1304 dataset_size: 166 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "0dc6521d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
amitness/sentiment-mt	2023-08-15T10:39:03.000Z	[ "language:mt", "region:us" ]	amitness	null	null	null	0	726	--- language: mt dataset_info: features: - name: label dtype: class_label: names: '0': negative '1': positive - name: text dtype: string splits: - name: train num_bytes: 83382 num_examples: 595 - name: validation num_bytes: 11602 num_examples: 85 - name: test num_bytes: 25749 num_examples: 171 download_size: 0 dataset_size: 120733 --- # Dataset Card for "sentiment-mt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thesistranslation/wmt14	2023-08-09T13:08:40.000Z	[ "region:us" ]	thesistranslation	null	@InProceedings{bojar-EtAl:2014:W14-33, author = {Bojar, Ondrej and Buck, Christian and Federmann, Christian and Haddow, Barry and Koehn, Philipp and Leveling, Johannes and Monz, Christof and Pecina, Pavel and Post, Matt and Saint-Amand, Herve and Soricut, Radu and Specia, Lucia and Tamchyna, Ale\v{s}}, title = {Findings of the 2014 Workshop on Statistical Machine Translation}, booktitle = {Proceedings of the Ninth Workshop on Statistical Machine Translation}, month = {June}, year = {2014}, address = {Baltimore, Maryland, USA}, publisher = {Association for Computational Linguistics}, pages = {12--58}, url = {http://www.aclweb.org/anthology/W/W14/W14-3302} }	null	0	722	# Aim of this dataset The code used to retrieve and create this dataset is almost identical to the one that you can find here [wmt14](https://huggingface.co/datasets/wmt14). We only added the possibility to retrieve the "es-en" translation pairs from the wmt13. Keep in mind that for this language pair the validation and test sets are the newstest2012 and newstest2013 respectively. Pay attention: some es-en pair sentences on the validation set contain the backslash followed by a double quote character (\\"). Thanks to the Huggingface team for all the work they have done!
sem_eval_2010_task_8	2023-04-05T13:39:59.000Z	[ "language:en", "region:us" ]	null	The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals. The task was designed to compare different approaches to semantic relation classification and to provide a standard testbed for future research.	@inproceedings{hendrickx-etal-2010-semeval, title = "{S}em{E}val-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals", author = "Hendrickx, Iris and Kim, Su Nam and Kozareva, Zornitsa and Nakov, Preslav and {\'O} S{\'e}aghdha, Diarmuid and Pad{\'o}, Sebastian and Pennacchiotti, Marco and Romano, Lorenza and Szpakowicz, Stan", booktitle = "Proceedings of the 5th International Workshop on Semantic Evaluation", month = jul, year = "2010", address = "Uppsala, Sweden", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/S10-1006", pages = "33--38", }	null	4	721	--- language: - en paperswithcode_id: semeval-2010-task-8 pretty_name: SemEval-2010 Task 8 dataset_info: features: - name: sentence dtype: string - name: relation dtype: class_label: names: '0': Cause-Effect(e1,e2) '1': Cause-Effect(e2,e1) '2': Component-Whole(e1,e2) '3': Component-Whole(e2,e1) '4': Content-Container(e1,e2) '5': Content-Container(e2,e1) '6': Entity-Destination(e1,e2) '7': Entity-Destination(e2,e1) '8': Entity-Origin(e1,e2) '9': Entity-Origin(e2,e1) '10': Instrument-Agency(e1,e2) '11': Instrument-Agency(e2,e1) '12': Member-Collection(e1,e2) '13': Member-Collection(e2,e1) '14': Message-Topic(e1,e2) '15': Message-Topic(e2,e1) '16': Product-Producer(e1,e2) '17': Product-Producer(e2,e1) '18': Other splits: - name: train num_bytes: 1054352 num_examples: 8000 - name: test num_bytes: 357075 num_examples: 2717 download_size: 1964087 dataset_size: 1411427 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: sentence: text relation: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "sem_eval_2010_task_8" ## 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://semeval2.fbk.eu/semeval2.php?location=tasks&taskid=11](https://semeval2.fbk.eu/semeval2.php?location=tasks&taskid=11) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - 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 downloaded dataset files: 1.96 MB - Size of the generated dataset: 1.42 MB - Total amount of disk used: 3.38 MB ### Dataset Summary The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals. The task was designed to compare different approaches to semantic relation classification and to provide a standard testbed for future research. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 1.96 MB - Size of the generated dataset: 1.42 MB - Total amount of disk used: 3.38 MB An example of 'train' looks as follows. ``` { "relation": 3, "sentence": "The system as described above has its greatest application in an arrayed <e1>configuration</e1> of antenna <e2>elements</e2>." } ``` ### Data Fields The data fields are the same among all splits. #### default - `sentence`: a `string` feature. - `relation`: a classification label, with possible values including `Cause-Effect(e1,e2)` (0), `Cause-Effect(e2,e1)` (1), `Component-Whole(e1,e2)` (2), `Component-Whole(e2,e1)` (3), `Content-Container(e1,e2)` (4). ### Data Splits \| name \|train\|test\| \|-------\|----:\|---:\| \|default\| 8000\|2717\| ## 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{hendrickx-etal-2010-semeval, title = "{S}em{E}val-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals", author = "Hendrickx, Iris and Kim, Su Nam and Kozareva, Zornitsa and Nakov, Preslav and {'O} S{'e}aghdha, Diarmuid and Pad{'o}, Sebastian and Pennacchiotti, Marco and Romano, Lorenza and Szpakowicz, Stan", booktitle = "Proceedings of the 5th International Workshop on Semantic Evaluation", month = jul, year = "2010", address = "Uppsala, Sweden", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/S10-1006", pages = "33--38", } ``` ### Contributions Thanks to [@JoelNiklaus](https://github.com/JoelNiklaus) for adding this dataset.
result-kand2-sdxl-wuerst-karlo/76e05263	2023-09-17T02:45:19.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	721	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 197 num_examples: 10 download_size: 1361 dataset_size: 197 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "76e05263" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deepset/prompt-injections	2023-07-31T15:04:06.000Z	[ "region:us" ]	deepset	null	null	null	15	720	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 71720 num_examples: 546 - name: test num_bytes: 15981 num_examples: 116 download_size: 51215 dataset_size: 87701 license: cc-by-4.0 --- # Dataset Card for "deberta-v3-base-injection-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liar	2023-01-25T14:34:21.000Z	[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "fake-news-detection", "arxiv:1705.00648", "region:us" ]	null	LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for all other labels range from 2,063 to 2,638. In each case, the labeler provides a lengthy analysis report to ground each judgment.	@inproceedings{wang-2017-liar, title = "{``}Liar, Liar Pants on Fire{''}: A New Benchmark Dataset for Fake News Detection", author = "Wang, William Yang", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-2067", doi = "10.18653/v1/P17-2067", pages = "422--426", abstract = "Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.", }	null	4	717	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: liar pretty_name: LIAR tags: - fake-news-detection dataset_info: features: - name: id dtype: string - name: label dtype: class_label: names: '0': 'false' '1': half-true '2': mostly-true '3': 'true' '4': barely-true '5': pants-fire - name: statement dtype: string - name: subject dtype: string - name: speaker dtype: string - name: job_title dtype: string - name: state_info dtype: string - name: party_affiliation dtype: string - name: barely_true_counts dtype: float32 - name: false_counts dtype: float32 - name: half_true_counts dtype: float32 - name: mostly_true_counts dtype: float32 - name: pants_on_fire_counts dtype: float32 - name: context dtype: string splits: - name: train num_bytes: 2730651 num_examples: 10269 - name: test num_bytes: 341414 num_examples: 1283 - name: validation num_bytes: 341592 num_examples: 1284 download_size: 1013571 dataset_size: 3413657 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: statement: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # 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://sites.cs.ucsb.edu/~william/ - Repository: - Paper: https://arxiv.org/abs/1705.00648 - Leaderboard: - Point of Contact: ### Dataset Summary LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for all other labels range from 2,063 to 2,638. In each case, the labeler provides a lengthy analysis report to ground each judgment. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.
EleutherAI/fever	2023-04-30T00:09:28.000Z	[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended\|wikipedia", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "knowledge-verification", "region:us" ]	EleutherAI	null	null	null	1	717	--- language: - en paperswithcode_id: fever annotations_creators: - crowdsourced language_creators: - found license: - cc-by-sa-3.0 - gpl-3.0 multilinguality: - monolingual pretty_name: FEVER size_categories: - 100K<n<1M source_datasets: - extended\|wikipedia task_categories: - text-classification task_ids: [] tags: - knowledge-verification dataset_info: - config_name: v1.0 features: - name: id dtype: int32 - name: label dtype: string - name: claim dtype: string - name: evidence_annotation_id dtype: int32 - name: evidence_id dtype: int32 - name: evidence_wiki_url dtype: string - name: evidence_sentence_id dtype: int32 splits: - name: train num_bytes: 24147163 num_examples: 263822 - name: dev num_bytes: 2696375 num_examples: 28625 - name: paper_dev num_bytes: 1348943 num_examples: 14475 - name: paper_test num_bytes: 1347432 num_examples: 14150 download_size: 44853972 dataset_size: 40043693 - config_name: v2.0 features: - name: id dtype: int32 - name: label dtype: string - name: claim dtype: string - name: evidence_annotation_id dtype: int32 - name: evidence_id dtype: int32 - name: evidence_wiki_url dtype: string - name: evidence_sentence_id dtype: int32 splits: - name: validation num_bytes: 306243 num_examples: 2384 download_size: 392466 dataset_size: 306243 - config_name: wiki_pages features: - name: id dtype: string - name: text dtype: string - name: lines dtype: string splits: - name: wikipedia_pages num_bytes: 7254115038 num_examples: 5416537 download_size: 1713485474 dataset_size: 7254115038 --- # Dataset Card for "fever" ## 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://fever.ai/](https://fever.ai/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - 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) ### Dataset Summary With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction. - FEVER Dataset: FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. - FEVER 2.0 Adversarial Attacks Dataset: The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of participants in the Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating adversarial examples that induce classification errors for the existing systems. Breakers submitted a dataset of up to 1000 instances with equal number of instances for each of the three classes (Supported, Refuted NotEnoughInfo). Only novel claims (i.e. not contained in the original FEVER dataset) were considered as valid entries to the shared task. The submissions were then manually evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER annotation guidelines requirements). ### Supported Tasks and Leaderboards The task is verification of textual claims against textual sources. When compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the passage to verify each claim is given, and in recent years it typically consists a single sentence, while in verification systems it is retrieved from a large set of documents in order to form the evidence. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances #### v1.0 - Size of downloaded dataset files: 44.86 MB - Size of the generated dataset: 40.05 MB - Total amount of disk used: 84.89 MB An example of 'train' looks as follows. ``` 'claim': 'Nikolaj Coster-Waldau worked with the Fox Broadcasting Company.', 'evidence_wiki_url': 'Nikolaj_Coster-Waldau', 'label': 'SUPPORTS', 'id': 75397, 'evidence_id': 104971, 'evidence_sentence_id': 7, 'evidence_annotation_id': 92206} ``` #### v2.0 - Size of downloaded dataset files: 0.39 MB - Size of the generated dataset: 0.30 MB - Total amount of disk used: 0.70 MB #### wiki_pages - Size of downloaded dataset files: 1.71 GB - Size of the generated dataset: 7.25 GB - Total amount of disk used: 8.97 GB An example of 'wikipedia_pages' looks as follows. ``` {'text': 'The following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world . ', 'lines': '0\tThe following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world .\n1\t', 'id': '1928_in_association_football'} ``` ### Data Fields The data fields are the same among all splits. #### v1.0 - `id`: a `int32` feature. - `label`: a `string` feature. - `claim`: a `string` feature. - `evidence_annotation_id`: a `int32` feature. - `evidence_id`: a `int32` feature. - `evidence_wiki_url`: a `string` feature. - `evidence_sentence_id`: a `int32` feature. #### v2.0 - `id`: a `int32` feature. - `label`: a `string` feature. - `claim`: a `string` feature. - `evidence_annotation_id`: a `int32` feature. - `evidence_id`: a `int32` feature. - `evidence_wiki_url`: a `string` feature. - `evidence_sentence_id`: a `int32` feature. #### wiki_pages - `id`: a `string` feature. - `text`: a `string` feature. - `lines`: a `string` feature. ### Data Splits #### v1.0 \| \| train \| dev \| paper_dev \| paper_test \| \|------\|-------:\|------:\|----------:\|-----------:\| \| v1.0 \| 311431 \| 37566 \| 18999 \| 18567 \| #### v2.0 \| \| validation \| \|------\|-----------:\| \| v2.0 \| 2384 \| #### wiki_pages \| \| wikipedia_pages \| \|------------\|----------------:\| \| wiki_pages \| 5416537 \| ## 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 FEVER license: ``` These data annotations incorporate material from Wikipedia, which is licensed pursuant to the Wikipedia Copyright Policy. These annotations are made available under the license terms described on the applicable Wikipedia article pages, or, where Wikipedia license terms are unavailable, under the Creative Commons Attribution-ShareAlike License (version 3.0), available at http://creativecommons.org/licenses/by-sa/3.0/ (collectively, the â€œLicense Termsâ€). You may not use these files except in compliance with the applicable License Terms. ``` ### Citation Information If you use "FEVER Dataset", please cite: ```bibtex @inproceedings{Thorne18Fever, author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit}, title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}}, booktitle = {NAACL-HLT}, year = {2018} } ``` If you use "FEVER 2.0 Adversarial Attacks Dataset", please cite: ```bibtex @inproceedings{Thorne19FEVER2, author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit}, title = {The {FEVER2.0} Shared Task}, booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}}, year = {2018} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
cdminix/libritts-aligned	2023-09-19T06:13:05.000Z	[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "annotations_creators:crowdsourced", "language:en", "license:cc-by-4.0", "speech", "audio", "automatic-speech-recognition", "text-to-speech", "arxiv:1904.02882", "arxiv:2211.16049", "region:us" ]	cdminix	Dataset used for loading TTS spectrograms and waveform audio with alignments and a number of configurable "measures", which are extracted from the raw audio.	@article{zen2019libritts, title={LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech}, author={Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui}, journal={Interspeech}, year={2019} } @article{https://doi.org/10.48550/arxiv.2211.16049, author = {Minixhofer, Christoph and Klejch, Ondřej and Bell, Peter}, title = {Evaluating and reducing the distance between synthetic and real speech distributions}, year = {2022} }	null	3	717	--- pretty_name: LibriTTS Corpus with Forced Alignments annotations_creators: - crowdsourced language: en tags: - speech - audio - automatic-speech-recognition - text-to-speech license: - cc-by-4.0 task_categories: - automatic-speech-recognition - text-to-speech extra_gated_prompt: "When using this dataset to download LibriTTS, you agree to the terms on https://www.openslr.org" --- > There is also an identical dataset for the new libritts-r dataset at [cdminix/libritts-r-aligned](https://huggingface.co/datasets/cdminix/libritts-r-aligned) # Dataset Card for LibriTTS with Forced Alignments (and Measures) This dataset downloads LibriTTS and preprocesses it on your machine to create alignments using [montreal forced aligner](https://montreal-forced-aligner.readthedocs.io/en/latest/). You need to run ``pip install alignments phones`` before using this dataset. When running this the first time, it can take an hour or two, but subsequent runs will be lightning fast. ## Requirements - ``pip install alignments phones`` (required) - ``pip install speech-collator`` (optional) ## Example Item ```json { 'id': '100_122655_000073_000002.wav', 'speaker': '100', 'text': 'the day after, diana and mary quitted it for distant b.', 'start': 0.0, 'end': 3.6500000953674316, 'phones': ['[SILENCE]', 'ð', 'ʌ', '[SILENCE]', 'd', 'eɪ', '[SILENCE]', 'æ', 'f', 't', 'ɜ˞', '[COMMA]', 'd', 'aɪ', 'æ', 'n', 'ʌ', '[SILENCE]', 'æ', 'n', 'd', '[SILENCE]', 'm', 'ɛ', 'ɹ', 'i', '[SILENCE]', 'k', 'w', 'ɪ', 't', 'ɪ', 'd', '[SILENCE]', 'ɪ', 't', '[SILENCE]', 'f', 'ɜ˞', '[SILENCE]', 'd', 'ɪ', 's', 't', 'ʌ', 'n', 't', '[SILENCE]', 'b', 'i', '[FULL STOP]'], 'phone_durations': [5, 2, 4, 0, 5, 13, 0, 16, 7, 5, 20, 2, 6, 9, 15, 4, 2, 0, 11, 3, 5, 0, 3, 8, 9, 8, 0, 13, 3, 5, 3, 6, 4, 0, 8, 5, 0, 9, 5, 0, 7, 5, 6, 7, 4, 5, 10, 0, 3, 35, 9], 'audio': '/dev/shm/metts/train-clean-360-alignments/100/100_122655_000073_000002.wav' } ``` The phones are IPA phones, and the phone durations are in frames (assuming a hop length of 256, sample rate of 22050 and window length of 1024). These attributes can be changed using the ``hop_length``, ``sample_rate`` and ``window_length`` arguments to ``LibriTTSAlign``. ## Data Collator This dataset comes with a data collator which can be used to create batches of data for training. It can be installed using ``pip install speech-collator`` ([MiniXC/speech-collator](https://www.github.com/MiniXC/speech-collator)) and can be used as follows: ```python import json from datasets import load_dataset from speech_collator import SpeechCollator from torch.utils.data import DataLoader dataset = load_dataset('cdminix/libritts-aligned', split="train") speaker2ixd = json.load(open("speaker2idx.json")) phone2ixd = json.load(open("phone2idx.json")) collator = SpeechCollator( speaker2ixd=speaker2idx, phone2ixd=phone2idx , ) dataloader = DataLoader(dataset, collate_fn=collator.collate_fn, batch_size=8) ``` You can either download the ``speaker2idx.json`` and ``phone2idx.json`` files from [here](https://huggingface.co/datasets/cdminix/libritts-aligned/tree/main/data) or create them yourself using the following code: ```python import json from datasets import load_dataset from speech_collator import SpeechCollator, create_speaker2idx, create_phone2idx dataset = load_dataset("cdminix/libritts-aligned", split="train") # Create speaker2idx and phone2idx speaker2idx = create_speaker2idx(dataset, unk_idx=0) phone2idx = create_phone2idx(dataset, unk_idx=0) # save to json with open("speaker2idx.json", "w") as f: json.dump(speaker2idx, f) with open("phone2idx.json", "w") as f: json.dump(phone2idx, f) ``` ### Measures When using ``speech-collator`` you can also use the ``measures`` argument to specify which measures to use. The following example extracts Pitch and Energy on the fly. ```python import json from torch.utils.data import DataLoader from datasets import load_dataset from speech_collator import SpeechCollator, create_speaker2idx, create_phone2idx from speech_collator.measures import PitchMeasure, EnergyMeasure dataset = load_dataset("cdminix/libritts-aligned", split="train") speaker2idx = json.load(open("data/speaker2idx.json")) phone2idx = json.load(open("data/phone2idx.json")) # Create SpeechCollator speech_collator = SpeechCollator( speaker2idx=speaker2idx, phone2idx=phone2idx, measures=[PitchMeasure(), EnergyMeasure()], return_keys=["measures"] ) # Create DataLoader dataloader = DataLoader( dataset, batch_size=8, collate_fn=speech_collator.collate_fn, ) ``` COMING SOON: Detailed documentation on how to use the measures at [MiniXC/speech-collator](https://www.github.com/MiniXC/speech-collator). ## Splits This dataset has the following splits: - ``train``: All the training data, except one sample per speaker which is used for validation. - ``dev``: The validation data, one sample per speaker. - ``train.clean.100``: Training set derived from the original materials of the train-clean-100 subset of LibriSpeech. - ``train.clean.360``: Training set derived from the original materials of the train-clean-360 subset of LibriSpeech. - ``train.other.500``: Training set derived from the original materials of the train-other-500 subset of LibriSpeech. - ``dev.clean``: Validation set derived from the original materials of the dev-clean subset of LibriSpeech. - ``dev.other``: Validation set derived from the original materials of the dev-other subset of LibriSpeech. - ``test.clean``: Test set derived from the original materials of the test-clean subset of LibriSpeech. - ``test.other``: Test set derived from the original materials of the test-other subset of LibriSpeech. ## Environment Variables There are a few environment variable which can be set. - ``LIBRITTS_VERBOSE``: If set, will print out more information about the dataset creation process. - ``LIBRITTS_MAX_WORKERS``: The number of workers to use when creating the alignments. Defaults to ``cpu_count()``. - ``LIBRITTS_PATH``: The path to download LibriTTS to. Defaults to the value of ``HF_DATASETS_CACHE``. # Citation When using LibriTTS please cite the following papers: - [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://arxiv.org/abs/1904.02882) - [Montreal Forced Aligner: Trainable text-speech alignment using Kaldi](https://www.researchgate.net/publication/319185277_Montreal_Forced_Aligner_Trainable_Text-Speech_Alignment_Using_Kaldi) When using the Measures please cite the following paper (ours): - [Evaluating and reducing the distance between synthetic and real speech distributions](https://arxiv.org/abs/2211.16049)
asset	2023-06-01T14:59:51.000Z	[ "task_categories:text-classification", "task_categories:text2text-generation", "task_ids:text-simplification", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "source_datasets:extended\|other-turkcorpus", "language:en", "license:cc-by-sa-4.0", "simplification-evaluation", "region:us" ]	null	ASSET is a dataset for evaluating Sentence Simplification systems with multiple rewriting transformations, as described in "ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations". The corpus is composed of 2000 validation and 359 test original sentences that were each simplified 10 times by different annotators. The corpus also contains human judgments of meaning preservation, fluency and simplicity for the outputs of several automatic text simplification systems.	@inproceedings{alva-manchego-etal-2020-asset, title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations", author = "Alva-Manchego, Fernando and Martin, Louis and Bordes, Antoine and Scarton, Carolina and Sagot, Benoit and Specia, Lucia", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.424", pages = "4668--4679", }	null	9	716	--- annotations_creators: - machine-generated language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original - extended\|other-turkcorpus task_categories: - text-classification - text2text-generation task_ids: - text-simplification paperswithcode_id: asset pretty_name: ASSET tags: - simplification-evaluation dataset_info: - config_name: simplification features: - name: original dtype: string - name: simplifications sequence: string splits: - name: validation num_bytes: 2303496 num_examples: 2000 - name: test num_bytes: 411031 num_examples: 359 download_size: 3639353 dataset_size: 2714527 - config_name: ratings features: - name: original dtype: string - name: simplification dtype: string - name: original_sentence_id dtype: int32 - name: aspect dtype: class_label: names: '0': meaning '1': fluency '2': simplicity - name: worker_id dtype: int32 - name: rating dtype: int32 splits: - name: full num_bytes: 1036853 num_examples: 4500 download_size: 3639353 dataset_size: 1036853 config_names: - ratings - simplification --- # Dataset Card for ASSET ## 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: [ASSET Github repository](https://github.com/facebookresearch/asset) - Paper: [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://www.aclweb.org/anthology/2020.acl-main.424/) - Point of Contact: [Louis Martin](louismartincs@gmail.com) ### Dataset Summary [ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus]( https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence splitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations. ### Supported Tasks and Leaderboards The dataset supports the evaluation of `text-simplification` systems. Success in this tasks is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf). ### Languages The text in this dataset is in English (`en`). ## Dataset Structure ### Data Instances - `simplification` configuration: an instance consists in an original sentence and 10 possible reference simplifications. - `ratings` configuration: a data instance consists in an original sentence, a simplification obtained by an automated system, and a judgment of quality along one of three axes by a crowd worker. ### Data Fields - `original`: an original sentence from the source datasets - `simplifications`: in the `simplification` config, a set of reference simplifications produced by crowd workers. - `simplification`: in the `ratings` config, a simplification of the original obtained by an automated system - `aspect`: in the `ratings` config, the aspect on which the simplification is evaluated, one of `meaning`, `fluency`, `simplicity` - `rating`: a quality rating between 0 and 100 ### Data Splits ASSET does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) for training. Each input sentence has 10 associated reference simplified sentences. The statistics of ASSET are given below. \| \| Dev \| Test \| Total \| \| ----- \| ------ \| ---- \| ----- \| \| Input Sentences \| 2000 \| 359 \| 2359 \| \| Reference Simplifications \| 20000 \| 3590 \| 23590 \| The test and validation sets are the same as those of TurkCorpus. The split was random. There are 19.04 tokens per reference on average (lower than 21.29 and 25.49 for TurkCorpus and HSplit, respectively). Most (17,245) of the referece sentences do not involve sentence splitting. ## Dataset Creation ### Curation Rationale ASSET was created in order to improve the evaluation of sentence simplification. It uses the same input sentences as the [TurkCorpus]( https://github.com/cocoxu/simplification/) dataset from [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). The 2,359 input sentences of TurkCorpus are a sample of "standard" (not simple) sentences from the [Parallel Wikipedia Simplification (PWKP)](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/sentence_simplification/simple_complex_sentence_pairs/index.en.jsp) dataset [(Zhu et al., 2010)](https://www.aclweb.org/anthology/C10-1152.pdf), which come from the August 22, 2009 version of Wikipedia. The sentences of TurkCorpus were chosen to be of similar length [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). No further information is provided on the sampling strategy. The TurkCorpus dataset was developed in order to overcome some of the problems with sentence pairs from Standard and Simple Wikipedia: a large fraction of sentences were misaligned, or not actually simpler [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). However, TurkCorpus mainly focused on lexical paraphrasing, and so cannot be used to evaluate simplifications involving compression (deletion) or sentence splitting. HSplit [(Sulem et al., 2018)](https://www.aclweb.org/anthology/D18-1081.pdf), on the other hand, can only be used to evaluate sentence splitting. The reference sentences in ASSET include a wider variety of sentence rewriting strategies, combining splitting, compression and paraphrasing. Annotators were given examples of each kind of transformation individually, as well as all three transformations used at once, but were allowed to decide which transformations to use for any given sentence. An example illustrating the differences between TurkCorpus, HSplit and ASSET is given below: > Original: He settled in London, devoting himself chiefly to practical teaching. > > TurkCorpus: He rooted in London, devoting himself mainly to practical teaching. > > HSplit: He settled in London. He devoted himself chiefly to practical teaching. > > ASSET: He lived in London. He was a teacher. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The input sentences are from English Wikipedia (August 22, 2009 version). No demographic information is available for the writers of these sentences. However, most Wikipedia editors are male (Lam, 2011; Graells-Garrido, 2015), which has an impact on the topics covered (see also [the Wikipedia page on Wikipedia gender bias](https://en.wikipedia.org/wiki/Gender_bias_on_Wikipedia)). In addition, Wikipedia editors are mostly white, young, and from the Northern Hemisphere [(Wikipedia: Systemic bias)](https://en.wikipedia.org/wiki/Wikipedia:Systemic_bias). Reference sentences were written by 42 workers on Amazon Mechanical Turk (AMT). The requirements for being an annotator were: - Passing a Qualification Test (appropriately simplifying sentences). Out of 100 workers, 42 passed the test. - Being a resident of the United States, United Kingdom or Canada. - Having a HIT approval rate over 95%, and over 1000 HITs approved. No other demographic or compensation information is provided in the ASSET paper. ### Annotations #### Annotation process The instructions given to the annotators are available [here](https://github.com/facebookresearch/asset/blob/master/crowdsourcing/AMT_AnnotationInstructions.pdf). #### 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 The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases (Schmahl et al., 2020) and racial biases (Adams et al., 2019). > Adams, Julia, Hannah Brückner, and Cambria Naslund. "Who Counts as a Notable Sociologist on Wikipedia? Gender, Race, and the “Professor Test”." Socius 5 (2019): 2378023118823946. > Schmahl, Katja Geertruida, et al. "Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings." Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science. 2020. ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators ASSET was developed by researchers at the University of Sheffield, Inria, Facebook AI Research, and Imperial College London. The work was partly supported by Benoît Sagot's chair in the PRAIRIE institute, funded by the French National Research Agency (ANR) as part of the "Investissements d’avenir" program (reference ANR-19-P3IA-0001). ### Licensing Information [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) ### Citation Information ``` @inproceedings{alva-manchego-etal-2020-asset, title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations", author = "Alva-Manchego, Fernando and Martin, Louis and Bordes, Antoine and Scarton, Carolina and Sagot, Beno{\^\i}t and Specia, Lucia", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.424", pages = "4668--4679", } ``` This dataset card uses material written by [Juan Diego Rodriguez](https://github.com/juand-r). ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
shibing624/nli_zh	2022-10-30T06:30:56.000Z	[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "annotations_creators:shibing624", "language_creators:shibing624", "multilinguality:monolingual", "size_categories:100K<n<20M", "source_datasets:https://github.com/shibing624/text2vec", "source_datasets:https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC", "source_datasets:http://icrc.hitsz.edu.cn/info/1037/1162.htm", "source_datasets:http://icrc.hitsz.edu.cn/Article/show/171.html", "source_datasets:https://arxiv.org/abs/1908.11828", "source_datasets:https://github.com/pluto-junzeng/CNSD", "language:zh", "license:cc-by-4.0", "arxiv:1908.11828", "region:us" ]	shibing624	纯文本数据，格式：（sentence1， sentence2， label）。常见中文语义匹配数据集，包含ATEC、BQ、LCQMC、PAWSX、STS-B共5个任务。	null	null	32	714	--- annotations_creators: - shibing624 language_creators: - shibing624 language: - zh license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<20M source_datasets: - https://github.com/shibing624/text2vec - https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC - http://icrc.hitsz.edu.cn/info/1037/1162.htm - http://icrc.hitsz.edu.cn/Article/show/171.html - https://arxiv.org/abs/1908.11828 - https://github.com/pluto-junzeng/CNSD task_categories: - text-classification task_ids: - natural-language-inference - semantic-similarity-scoring - text-scoring paperswithcode_id: snli pretty_name: Stanford Natural Language Inference --- # Dataset Card for NLI_zh ## 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: [Chinese NLI dataset](https://github.com/shibing624/text2vec) - Leaderboard: [NLI_zh leaderboard](https://github.com/shibing624/text2vec) (located on the homepage) - Size of downloaded dataset files: 16 MB - Total amount of disk used: 42 MB ### Dataset Summary 常见中文语义匹配数据集，包含[ATEC](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC)、[BQ](http://icrc.hitsz.edu.cn/info/1037/1162.htm)、[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)、[PAWSX](https://arxiv.org/abs/1908.11828)、[STS-B](https://github.com/pluto-junzeng/CNSD)共5个任务。数据源： - ATEC: https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC - BQ: http://icrc.hitsz.edu.cn/info/1037/1162.htm - LCQMC: http://icrc.hitsz.edu.cn/Article/show/171.html - PAWSX: https://arxiv.org/abs/1908.11828 - STS-B: https://github.com/pluto-junzeng/CNSD ### Supported Tasks and Leaderboards Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。中文匹配任务的结果目前在顶会paper上出现较少，我罗列一个我自己训练的结果： Leaderboard: [NLI_zh leaderboard](https://github.com/shibing624/text2vec) ### Languages 数据集均是简体中文文本。 ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` { "sentence1": "刘诗诗杨幂谁漂亮", "sentence2": "刘诗诗和杨幂谁漂亮", "label": 1, } { "sentence1": "汇理财怎么样", "sentence2": "怎么样去理财", "label": 0, } ``` ### Data Fields The data fields are the same among all splits. - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including `similarity` (1), `dissimilarity` (0). ### Data Splits #### ATEC ```shell $ wc -l ATEC/* 20000 ATEC/ATEC.test.data 62477 ATEC/ATEC.train.data 20000 ATEC/ATEC.valid.data 102477 total ``` #### BQ ```shell $ wc -l BQ/* 10000 BQ/BQ.test.data 100000 BQ/BQ.train.data 10000 BQ/BQ.valid.data 120000 total ``` #### LCQMC ```shell $ wc -l LCQMC/* 12500 LCQMC/LCQMC.test.data 238766 LCQMC/LCQMC.train.data 8802 LCQMC/LCQMC.valid.data 260068 total ``` #### PAWSX ```shell $ wc -l PAWSX/* 2000 PAWSX/PAWSX.test.data 49401 PAWSX/PAWSX.train.data 2000 PAWSX/PAWSX.valid.data 53401 total ``` #### STS-B ```shell $ wc -l STS-B/* 1361 STS-B/STS-B.test.data 5231 STS-B/STS-B.train.data 1458 STS-B/STS-B.valid.data 8050 total ``` ## Dataset Creation ### Curation Rationale 作为中文NLI(natural langauge inference)数据集，这里把这个数据集上传到huggingface的datasets，方便大家使用。 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? 数据集的版权归原作者所有，使用各数据集时请尊重原数据集的版权。 BQ: Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang, The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification EMNLP2018. ### Annotations #### Annotation process #### Who are the annotators? 原作者。 ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. Systems that are successful at such a task may be more successful in modeling semantic representations. ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators - 苏剑林对文件名称有整理 - 我上传到huggingface的datasets ### Licensing Information 用于学术研究。 The BQ corpus is free to the public for academic research. ### Contributions Thanks to [@shibing624](https://github.com/shibing624) add this dataset.
open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3	2023-08-27T12:30:19.000Z	[ "region:us" ]	open-llm-leaderboard	null	null	null	0	713	--- 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 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-07-19T16:22:02.219224](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3/blob/main/results_2023-07-19T16%3A22%3A02.219224.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\": 0.4829612438141863,\n\ \ \"acc_stderr\": 0.035041389482858204,\n \"acc_norm\": 0.48663764074426863,\n\ \ \"acc_norm_stderr\": 0.03502942029152831,\n \"mc1\": 0.31701346389228885,\n\ \ \"mc1_stderr\": 0.016289203374403392,\n \"mc2\": 0.47006281499614255,\n\ \ \"mc2_stderr\": 0.015102334330899319\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.47696245733788395,\n \"acc_stderr\": 0.014595873205358264,\n\ \ \"acc_norm\": 0.5042662116040956,\n \"acc_norm_stderr\": 0.014610858923956955\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5795658235411273,\n\ \ \"acc_stderr\": 0.004926198483948701,\n \"acc_norm\": 0.7691694881497709,\n\ \ \"acc_norm_stderr\": 0.004205030476886542\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.42962962962962964,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.4868421052631579,\n \"acc_stderr\": 0.04067533136309173,\n\ \ \"acc_norm\": 0.4868421052631579,\n \"acc_norm_stderr\": 0.04067533136309173\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.49,\n\ \ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.539622641509434,\n \"acc_stderr\": 0.030676096599389184,\n\ \ \"acc_norm\": 0.539622641509434,\n \"acc_norm_stderr\": 0.030676096599389184\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5069444444444444,\n\ \ \"acc_stderr\": 0.04180806750294938,\n \"acc_norm\": 0.5069444444444444,\n\ \ \"acc_norm_stderr\": 0.04180806750294938\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.3930635838150289,\n\ \ \"acc_stderr\": 0.03724249595817728,\n \"acc_norm\": 0.3930635838150289,\n\ \ \"acc_norm_stderr\": 0.03724249595817728\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.3617021276595745,\n \"acc_stderr\": 0.03141082197596239,\n\ \ \"acc_norm\": 0.3617021276595745,\n \"acc_norm_stderr\": 0.03141082197596239\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.21929824561403508,\n\ \ \"acc_stderr\": 0.03892431106518753,\n \"acc_norm\": 0.21929824561403508,\n\ \ \"acc_norm_stderr\": 0.03892431106518753\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3253968253968254,\n \"acc_stderr\": 0.02413015829976261,\n \"\ acc_norm\": 0.3253968253968254,\n \"acc_norm_stderr\": 0.02413015829976261\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3412698412698413,\n\ \ \"acc_stderr\": 0.042407993275749255,\n \"acc_norm\": 0.3412698412698413,\n\ \ \"acc_norm_stderr\": 0.042407993275749255\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5161290322580645,\n\ \ \"acc_stderr\": 0.028429203176724555,\n \"acc_norm\": 0.5161290322580645,\n\ \ \"acc_norm_stderr\": 0.028429203176724555\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.37438423645320196,\n \"acc_stderr\": 0.03405155380561952,\n\ \ \"acc_norm\": 0.37438423645320196,\n \"acc_norm_stderr\": 0.03405155380561952\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\"\ : 0.41,\n \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.5878787878787879,\n \"acc_stderr\": 0.03843566993588717,\n\ \ \"acc_norm\": 0.5878787878787879,\n \"acc_norm_stderr\": 0.03843566993588717\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6262626262626263,\n \"acc_stderr\": 0.034468977386593325,\n \"\ acc_norm\": 0.6262626262626263,\n \"acc_norm_stderr\": 0.034468977386593325\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7202072538860104,\n \"acc_stderr\": 0.03239637046735704,\n\ \ \"acc_norm\": 0.7202072538860104,\n \"acc_norm_stderr\": 0.03239637046735704\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.47692307692307695,\n \"acc_stderr\": 0.025323990861736118,\n\ \ \"acc_norm\": 0.47692307692307695,\n \"acc_norm_stderr\": 0.025323990861736118\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712163,\n \ \ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712163\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.4369747899159664,\n \"acc_stderr\": 0.03221943636566196,\n \ \ \"acc_norm\": 0.4369747899159664,\n \"acc_norm_stderr\": 0.03221943636566196\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.6293577981651376,\n \"acc_stderr\": 0.02070745816435298,\n \"\ acc_norm\": 0.6293577981651376,\n \"acc_norm_stderr\": 0.02070745816435298\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.6225490196078431,\n \"acc_stderr\": 0.034022720443407026,\n \"\ acc_norm\": 0.6225490196078431,\n \"acc_norm_stderr\": 0.034022720443407026\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.6371308016877637,\n \"acc_stderr\": 0.031299208255302136,\n \ \ \"acc_norm\": 0.6371308016877637,\n \"acc_norm_stderr\": 0.031299208255302136\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.57847533632287,\n\ \ \"acc_stderr\": 0.03314190222110658,\n \"acc_norm\": 0.57847533632287,\n\ \ \"acc_norm_stderr\": 0.03314190222110658\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5725190839694656,\n \"acc_stderr\": 0.04338920305792401,\n\ \ \"acc_norm\": 0.5725190839694656,\n \"acc_norm_stderr\": 0.04338920305792401\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6776859504132231,\n \"acc_stderr\": 0.04266416363352168,\n \"\ acc_norm\": 0.6776859504132231,\n \"acc_norm_stderr\": 0.04266416363352168\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.5521472392638037,\n \"acc_stderr\": 0.03906947479456606,\n\ \ \"acc_norm\": 0.5521472392638037,\n \"acc_norm_stderr\": 0.03906947479456606\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.29464285714285715,\n\ \ \"acc_stderr\": 0.043270409325787275,\n \"acc_norm\": 0.29464285714285715,\n\ \ \"acc_norm_stderr\": 0.043270409325787275\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6310679611650486,\n \"acc_stderr\": 0.0477761518115674,\n\ \ \"acc_norm\": 0.6310679611650486,\n \"acc_norm_stderr\": 0.0477761518115674\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.717948717948718,\n\ \ \"acc_stderr\": 0.029480360549541194,\n \"acc_norm\": 0.717948717948718,\n\ \ \"acc_norm_stderr\": 0.029480360549541194\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.6551724137931034,\n\ \ \"acc_stderr\": 0.01699712334611344,\n \"acc_norm\": 0.6551724137931034,\n\ \ \"acc_norm_stderr\": 0.01699712334611344\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5202312138728323,\n \"acc_stderr\": 0.026897049996382868,\n\ \ \"acc_norm\": 0.5202312138728323,\n \"acc_norm_stderr\": 0.026897049996382868\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5326797385620915,\n \"acc_stderr\": 0.028568699752225868,\n\ \ \"acc_norm\": 0.5326797385620915,\n \"acc_norm_stderr\": 0.028568699752225868\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5337620578778135,\n\ \ \"acc_stderr\": 0.02833327710956279,\n \"acc_norm\": 0.5337620578778135,\n\ \ \"acc_norm_stderr\": 0.02833327710956279\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.558641975308642,\n \"acc_stderr\": 0.02762873715566877,\n\ \ \"acc_norm\": 0.558641975308642,\n \"acc_norm_stderr\": 0.02762873715566877\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611317,\n \ \ \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611317\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.363754889178618,\n\ \ \"acc_stderr\": 0.012286991879902896,\n \"acc_norm\": 0.363754889178618,\n\ \ \"acc_norm_stderr\": 0.012286991879902896\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.46691176470588236,\n \"acc_stderr\": 0.030306257722468317,\n\ \ \"acc_norm\": 0.46691176470588236,\n \"acc_norm_stderr\": 0.030306257722468317\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.4362745098039216,\n \"acc_stderr\": 0.020062874243539128,\n \ \ \"acc_norm\": 0.4362745098039216,\n \"acc_norm_stderr\": 0.020062874243539128\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04789131426105757,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04789131426105757\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.5510204081632653,\n \"acc_stderr\": 0.03184213866687579,\n\ \ \"acc_norm\": 0.5510204081632653,\n \"acc_norm_stderr\": 0.03184213866687579\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6766169154228856,\n\ \ \"acc_stderr\": 0.03307615947979034,\n \"acc_norm\": 0.6766169154228856,\n\ \ \"acc_norm_stderr\": 0.03307615947979034\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.3674698795180723,\n\ \ \"acc_stderr\": 0.03753267402120575,\n \"acc_norm\": 0.3674698795180723,\n\ \ \"acc_norm_stderr\": 0.03753267402120575\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.6608187134502924,\n \"acc_stderr\": 0.03631053496488905,\n\ \ \"acc_norm\": 0.6608187134502924,\n \"acc_norm_stderr\": 0.03631053496488905\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.31701346389228885,\n\ \ \"mc1_stderr\": 0.016289203374403392,\n \"mc2\": 0.47006281499614255,\n\ \ \"mc2_stderr\": 0.015102334330899319\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: clementine@hf.co 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_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: results data_files: - split: 2023_07_19T16_22_02.219224 path: - results_2023-07-19T16:22:02.219224.parquet - split: latest path: - results_2023-07-19T16:22:02.219224.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: clementine@hf.co ### 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 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-07-19T16:22:02.219224](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.3/blob/main/results_2023-07-19T16%3A22%3A02.219224.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": 0.4829612438141863, "acc_stderr": 0.035041389482858204, "acc_norm": 0.48663764074426863, "acc_norm_stderr": 0.03502942029152831, "mc1": 0.31701346389228885, "mc1_stderr": 0.016289203374403392, "mc2": 0.47006281499614255, "mc2_stderr": 0.015102334330899319 }, "harness\|arc:challenge\|25": { "acc": 0.47696245733788395, "acc_stderr": 0.014595873205358264, "acc_norm": 0.5042662116040956, "acc_norm_stderr": 0.014610858923956955 }, "harness\|hellaswag\|10": { "acc": 0.5795658235411273, "acc_stderr": 0.004926198483948701, "acc_norm": 0.7691694881497709, "acc_norm_stderr": 0.004205030476886542 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.4868421052631579, "acc_stderr": 0.04067533136309173, "acc_norm": 0.4868421052631579, "acc_norm_stderr": 0.04067533136309173 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.539622641509434, "acc_stderr": 0.030676096599389184, "acc_norm": 0.539622641509434, "acc_norm_stderr": 0.030676096599389184 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5069444444444444, "acc_stderr": 0.04180806750294938, "acc_norm": 0.5069444444444444, "acc_norm_stderr": 0.04180806750294938 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.44, "acc_stderr": 0.049888765156985884, "acc_norm": 0.44, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.3930635838150289, "acc_stderr": 0.03724249595817728, "acc_norm": 0.3930635838150289, "acc_norm_stderr": 0.03724249595817728 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.3617021276595745, "acc_stderr": 0.03141082197596239, "acc_norm": 0.3617021276595745, "acc_norm_stderr": 0.03141082197596239 }, "harness\|hendrycksTest-econometrics\|5": { 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0.03631053496488905, "acc_norm": 0.6608187134502924, "acc_norm_stderr": 0.03631053496488905 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.31701346389228885, "mc1_stderr": 0.016289203374403392, "mc2": 0.47006281499614255, "mc2_stderr": 0.015102334330899319 } } ``` ### 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]
NumbersStation/NSText2SQL	2023-07-11T05:26:13.000Z	[ "task_categories:text2text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "language:en", "license:other", "text-to-sql", "region:us" ]	NumbersStation	null	null	null	24	712	--- language: - en task_categories: - text2text-generation license: - other language_creators: - crowdsourced - expert-generated multilinguality: - multilingual tags: - text-to-sql size_categories: - 100K<n<1M pretty_name: NSText2SQL --- # Dataset Summary NSText2SQL dataset used to train [NSQL](https://huggingface.co/NumbersStation/nsql-6B) models. The data is curated from more than 20 different public sources across the web with permissable licenses (listed below). All of these datasets come with existing text-to-SQL pairs. We apply various data cleaning and pre-processing techniques including table schema augmentation, SQL cleaning, and instruction generation using existing LLMs. The resulting dataset contains around 290,000 samples of text-to-SQL pairs. For more information and code, please see [this repository](https://github.com/NumbersStationAI/NSQL). # How to use it ```python from datasets import load_dataset dataset = load_dataset("NumbersStation/NSText2SQL") ``` # Dataset Structure ## Data Instances Each data instance in this dataset represents a text-to-SQL entry where the instruction has been formatted with the table schema and question. The output is the SQL in SQlite dialect. ## Data Fields - `instruction` (string): the instruction to generate SQL. - `output` (string): the ground truth SQL. - `source` (string): the source dataset of the sample. # Languages The language of the data is primarily English. # Source Data and Licensing Information NSText2SQL is sourced from repositories with various licenses. Any use of all or part of the data gathered in NSText2SQL must abide by the terms of the original licenses, including attribution clauses when relevant. We thank all authors who provided these datasets. We provide provenance information for each dataset below. \| Datasets \| License \| Link \| \| ---------------------- \| ------------ \| -------------------------------------------------------------------------------------------------------------------- \| \| academic \| Not Found \| [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) \| \| advising \| CC-BY-4.0 \| [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) \| \| atis \| Not Found \| [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) \| \| restaurants \| Not Found \| [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) \| \| scholar \| Not Found \| [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) \| \| imdb \| Not Found \| [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) \| \| yelp \| Not Found \| [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) \| \| criteria2sql \| Apache-2.0 \| [https://github.com/xiaojingyu92/Criteria2SQL](https://github.com/xiaojingyu92/Criteria2SQL) \| \| css \| CC-BY-4.0 \| [https://huggingface.co/datasets/zhanghanchong/css](https://huggingface.co/datasets/zhanghanchong/css) \| \| eICU \| CC-BY-4.0 \| [https://github.com/glee4810/EHRSQL](https://github.com/glee4810/EHRSQL) \| \| mimic_iii \| CC-BY-4.0 \| [https://github.com/glee4810/EHRSQL](https://github.com/glee4810/EHRSQL) \| \| geonucleardata \| CC-BY-SA-4.0 \| [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) \| \| greatermanchestercrime \| CC-BY-SA-4.0 \| [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) \| \| studentmathscore \| CC-BY-SA-4.0 \| [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) \| \| thehistoryofbaseball \| CC-BY-SA-4.0 \| [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) \| \| uswildfires \| CC-BY-SA-4.0 \| [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) \| \| whatcdhiphop \| CC-BY-SA-4.0 \| [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) \| \| worldsoccerdatabase \| CC-BY-SA-4.0 \| [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) \| \| pesticide \| CC-BY-SA-4.0 \| [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) \| \| mimicsql_data \| MIT \| [https://github.com/wangpinggl/TREQS](https://github.com/wangpinggl/TREQS) \| \| nvbench \| MIT \| [https://github.com/TsinghuaDatabaseGroup/nvBench](https://github.com/TsinghuaDatabaseGroup/nvBench) \| \| sede \| Apache-2.0 \| [https://github.com/hirupert/sede](https://github.com/hirupert/sede) \| \| spider \| CC-BY-SA-4.0 \| [https://huggingface.co/datasets/spider](https://huggingface.co/datasets/spider) \| \| sql_create_context \| CC-BY-4.0 \| [https://huggingface.co/datasets/b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) \| \| squall \| CC-BY-SA-4.0 \| [https://github.com/tzshi/squall](https://github.com/tzshi/squall) \| \| wikisql \| BSD 3-Clause \| [https://github.com/salesforce/WikiSQL](https://github.com/salesforce/WikiSQL) \| # Citing this work If you use this data in your work, please cite our work _and_ the appropriate original sources: To cite NSText2SQL, please use: ```TeX @software{numbersstation2023NSText2SQL, author = {Numbers Station Labs}, title = {NSText2SQL: An Open Source Text-to-SQL Dataset for Foundation Model Training}, month = {July}, year = {2023}, url = {https://github.com/NumbersStationAI/NSQL}, } ``` To cite dataset used in this work, please use: \| Datasets \| Cite \| \| ---------------------- \| ---------------------------------------------------------------------------------------- \| \| academic \| `\cite{data-advising,data-academic}` \| \| advising \| `\cite{data-advising}` \| \| atis \| `\cite{data-advising,data-atis-original,data-atis-geography-scholar}` \| \| restaurants \| `\cite{data-advising,data-restaurants-logic,data-restaurants-original,data-restaurants}` \| \| scholar \| `\cite{data-advising,data-atis-geography-scholar}` \| \| imdb \| `\cite{data-advising,data-imdb-yelp}` \| \| yelp \| `\cite{data-advising,data-imdb-yelp}` \| \| criteria2sql \| `\cite{Criteria-to-SQL}` \| \| css \| `\cite{zhang2023css}` \| \| eICU \| `\cite{lee2022ehrsql}` \| \| mimic_iii \| `\cite{lee2022ehrsql}` \| \| geonucleardata \| `\cite{lee-2021-kaggle-dbqa}` \| \| greatermanchestercrime \| `\cite{lee-2021-kaggle-dbqa}` \| \| studentmathscore \| `\cite{lee-2021-kaggle-dbqa}` \| \| thehistoryofbaseball \| `\cite{lee-2021-kaggle-dbqa}` \| \| uswildfires \| `\cite{lee-2021-kaggle-dbqa}` \| \| whatcdhiphop \| `\cite{lee-2021-kaggle-dbqa}` \| \| worldsoccerdatabase \| `\cite{lee-2021-kaggle-dbqa}` \| \| pesticide \| `\cite{lee-2021-kaggle-dbqa}` \| \| mimicsql_data \| `\cite{wang2020text}` \| \| nvbench \| `\cite{nvBench_SIGMOD21}` \| \| sede \| `\cite{hazoom2021text}` \| \| spider \| `\cite{data-spider}` \| \| sql_create_context \| Not Found \| \| squall \| `\cite{squall}` \| \| wikisql \| `\cite{data-wikisql}` \| ```TeX @InProceedings{data-advising, dataset = {Advising}, author = {Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev}, title = {Improving Text-to-SQL Evaluation Methodology}, booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {July}, year = {2018}, location = {Melbourne, Victoria, Australia}, pages = {351--360}, url = {http://aclweb.org/anthology/P18-1033}, } @InProceedings{data-imdb-yelp, dataset = {IMDB and Yelp}, author = {Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, and Thomas Dillig}, title = {SQLizer: Query Synthesis from Natural Language}, booktitle = {International Conference on Object-Oriented Programming, Systems, Languages, and Applications, ACM}, month = {October}, year = {2017}, pages = {63:1--63:26}, url = {http://doi.org/10.1145/3133887}, } @article{data-academic, dataset = {Academic}, author = {Fei Li and H. V. Jagadish}, title = {Constructing an Interactive Natural Language Interface for Relational Databases}, journal = {Proceedings of the VLDB Endowment}, volume = {8}, number = {1}, month = {September}, year = {2014}, pages = {73--84}, url = {http://dx.doi.org/10.14778/2735461.2735468}, } @InProceedings{data-atis-geography-scholar, dataset = {Scholar, and Updated ATIS and Geography}, author = {Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer}, title = {Learning a Neural Semantic Parser from User Feedback}, booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, year = {2017}, pages = {963--973}, location = {Vancouver, Canada}, url = {http://www.aclweb.org/anthology/P17-1089}, } @article{data-atis-original, dataset = {ATIS, original}, author = {Deborah A. Dahl, Madeleine Bates, Michael Brown, William Fisher, Kate Hunicke-Smith, David Pallett, Christine Pao, Alexander Rudnicky, and Elizabeth Shriber}, title = {{Expanding the scope of the ATIS task: The ATIS-3 corpus}}, journal = {Proceedings of the workshop on Human Language Technology}, year = {1994}, pages = {43--48}, url = {http://dl.acm.org/citation.cfm?id=1075823}, } @inproceedings{data-restaurants-logic, author = {Lappoon R. Tang and Raymond J. Mooney}, title = {Automated Construction of Database Interfaces: Intergrating Statistical and Relational Learning for Semantic Parsing}, booktitle = {2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora}, year = {2000}, pages = {133--141}, location = {Hong Kong, China}, url = {http://www.aclweb.org/anthology/W00-1317}, } @inproceedings{data-restaurants-original, author = {Ana-Maria Popescu, Oren Etzioni, and Henry Kautz}, title = {Towards a Theory of Natural Language Interfaces to Databases}, booktitle = {Proceedings of the 8th International Conference on Intelligent User Interfaces}, year = {2003}, location = {Miami, Florida, USA}, pages = {149--157}, url = {http://doi.acm.org/10.1145/604045.604070}, } @inproceedings{data-restaurants, author = {Alessandra Giordani and Alessandro Moschitti}, title = {Automatic Generation and Reranking of SQL-derived Answers to NL Questions}, booktitle = {Proceedings of the Second International Conference on Trustworthy Eternal Systems via Evolving Software, Data and Knowledge}, year = {2012}, location = {Montpellier, France}, pages = {59--76}, url = {https://doi.org/10.1007/978-3-642-45260-4_5}, } @InProceedings{data-spider, author = {Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev}, title = {Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, location = {Brussels, Belgium}, pages = {3911--3921}, url = {http://aclweb.org/anthology/D18-1425}, } @article{data-wikisql, author = {Victor Zhong, Caiming Xiong, and Richard Socher}, title = {Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning}, year = {2017}, journal = {CoRR}, volume = {abs/1709.00103}, } @InProceedings{Criteria-to-SQL, author = {Yu, Xiaojing and Chen, Tianlong and Yu, Zhengjie and Li, Huiyu and Yang, Yang and Jiang, Xiaoqian and Jiang, Anxiao}, title = {Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, month = {May}, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {5831--5839}, } @misc{zhang2023css, title = {CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset}, author = {Hanchong Zhang and Jieyu Li and Lu Chen and Ruisheng Cao and Yunyan Zhang and Yu Huang and Yefeng Zheng and Kai Yu}, year = {2023}, } @article{lee2022ehrsql, title = {EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records}, author = {Lee, Gyubok and Hwang, Hyeonji and Bae, Seongsu and Kwon, Yeonsu and Shin, Woncheol and Yang, Seongjun and Seo, Minjoon and Kim, Jong-Yeup and Choi, Edward}, journal = {Advances in Neural Information Processing Systems}, volume = {35}, pages = {15589--15601}, year = {2022}, } @inproceedings{lee-2021-kaggle-dbqa, title = {KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers}, author = {Lee, Chia-Hsuan and Polozov, Oleksandr and Richardson, Matthew}, booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)}, pages = {2261--2273}, year = {2021}, } @inproceedings{squall, title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries}, author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee}, booktitle = {Findings of EMNLP}, year = {2020}, } @article{hazoom2021text, title = {Text-to-SQL in the wild: a naturally-occurring dataset based on Stack exchange data}, author = {Hazoom, Moshe and Malik, Vibhor and Bogin, Ben}, journal = {arXiv preprint arXiv:2106.05006}, year = {2021}, } @inproceedings{wang2020text, title = {Text-to-SQL Generation for Question Answering on Electronic Medical Records}, author = {Wang, Ping and Shi, Tian and Reddy, Chandan K}, booktitle = {Proceedings of The Web Conference 2020}, pages = {350--361}, year = {2020}, } @inproceedings{nvBench_SIGMOD21, title = {Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks}, author = {Yuyu Luo and Nan Tang and Guoliang Li and Chengliang Chai and Wenbo Li and Xuedi Qin}, booktitle = {Proceedings of the 2021 International Conference on Management of Data, {SIGMOD} Conference 2021, June 20–25, 2021, Virtual Event, China}, publisher = {ACM}, year = {2021}, } ```
mozilla-foundation/common_voice_6_1	2023-07-29T16:00:07.000Z	[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended\|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]	mozilla-foundation	null	@inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 }	null	4	710	--- annotations_creators: - crowdsourced language_creators: - crowdsourced license: - cc0-1.0 multilinguality: - multilingual size_categories: ab: - n<1K ar: - 10K<n<100K as: - n<1K br: - 10K<n<100K ca: - 100K<n<1M cnh: - 1K<n<10K cs: - 10K<n<100K cv: - 10K<n<100K cy: - 10K<n<100K de: - 100K<n<1M dv: - 10K<n<100K el: - 10K<n<100K en: - 1M<n<10M eo: - 10K<n<100K es: - 100K<n<1M et: - 10K<n<100K eu: - 10K<n<100K fa: - 100K<n<1M fi: - 1K<n<10K fr: - 100K<n<1M fy-NL: - 10K<n<100K ga-IE: - 1K<n<10K hi: - n<1K hsb: - 1K<n<10K hu: - 1K<n<10K ia: - 1K<n<10K id: - 10K<n<100K it: - 100K<n<1M ja: - 1K<n<10K ka: - 1K<n<10K kab: - 100K<n<1M ky: - 10K<n<100K lg: - 1K<n<10K lt: - 1K<n<10K lv: - 1K<n<10K mn: - 10K<n<100K mt: - 10K<n<100K nl: - 10K<n<100K or: - 1K<n<10K pa-IN: - 1K<n<10K pl: - 100K<n<1M pt: - 10K<n<100K rm-sursilv: - 1K<n<10K rm-vallader: - 1K<n<10K ro: - 1K<n<10K ru: - 10K<n<100K rw: - 1M<n<10M sah: - 1K<n<10K sl: - 1K<n<10K sv-SE: - 10K<n<100K ta: - 10K<n<100K th: - 10K<n<100K tr: - 10K<n<100K tt: - 10K<n<100K uk: - 10K<n<100K vi: - 1K<n<10K vot: - n<1K zh-CN: - 10K<n<100K zh-HK: - 10K<n<100K zh-TW: - 10K<n<100K source_datasets: - extended\|common_voice paperswithcode_id: common-voice pretty_name: Common Voice Corpus 6.1 language_bcp47: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa-IN - pl - pt - rm-sursilv - rm-vallader - ro - ru - rw - sah - sl - sv-SE - ta - th - tr - tt - uk - vi - vot - zh-CN - zh-HK - zh-TW extra_gated_prompt: By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset. task_categories: - automatic-speech-recognition --- # Dataset Card for Common Voice Corpus 6.1 ## 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://commonvoice.mozilla.org/en/datasets - Repository: https://github.com/common-voice/common-voice - Paper: https://arxiv.org/abs/1912.06670 - Leaderboard: https://paperswithcode.com/dataset/common-voice - Point of Contact: [Anton Lozhkov](mailto:anton@huggingface.co) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 7335 validated hours in 60 languages, but more voices and languages are always added. Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the [🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) ### Languages ``` Abkhaz, Arabic, Assamese, Basque, Breton, Catalan, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, Finnish, French, Frisian, Georgian, German, Greek, Hakha Chin, Hindi, Hungarian, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Lithuanian, Luganda, Maltese, Mongolian, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Slovenian, Sorbian, Upper, Spanish, Swedish, Tamil, Tatar, Thai, Turkish, Ukrainian, Vietnamese, Votic, Welsh ``` ## Dataset Structure ### Data Instances A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): 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` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ```python from datasets import load_dataset ds = load_dataset("mozilla-foundation/common_voice_6_1", "en", use_auth_token=True) def prepare_dataset(batch): """Function to preprocess the dataset with the .map method""" transcription = batch["sentence"] if transcription.startswith('"') and transcription.endswith('"'): # we can remove trailing quotation marks as they do not affect the transcription transcription = transcription[1:-1] if transcription[-1] not in [".", "?", "!"]: # append a full-stop to sentences that do not end in punctuation transcription = transcription + "." batch["sentence"] = transcription return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` ## 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 The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```
nahyeon00/SQUAD	2023-07-19T08:51:16.000Z	[ "region:us" ]	nahyeon00	null	null	null	0	710	Entry not found
Jean-Baptiste/wikiner_fr	2023-06-26T15:33:17.000Z	[ "task_categories:token-classification", "language:fr", "region:us" ]	Jean-Baptiste	null	null	null	3	709	--- language: - fr dataset_info: features: - name: id dtype: int64 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': LOC '2': PER '3': MISC '4': ORG splits: - name: test num_bytes: 5954708 num_examples: 13410 - name: train num_bytes: 54305659 num_examples: 120682 download_size: 12147768 dataset_size: 60260367 train-eval-index: - config: Jean-Baptiste--wikiner_fr task: token-classification task_id: entity_extraction splits: eval_split: test col_mapping: tokens: tokens ner_tags: tags task_categories: - token-classification --- # Dataset Card for "wikiner_fr" Dataset Description: - Homepage: https://metatext.io/datasets/wikiner - Repository: - Paper: https://www.sciencedirect.com/science/article/pii/S0004370212000276?via%3Dihub - Leaderboard: - Point of Contact:
result-kand2-sdxl-wuerst-karlo/b73eb60b	2023-09-17T14:04:09.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	709	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 206 num_examples: 10 download_size: 1380 dataset_size: 206 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b73eb60b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wiki40b	2023-04-05T13:43:07.000Z	[ "language:en", "region:us" ]	null	Clean-up text for 40+ Wikipedia languages editions of pages correspond to entities. The datasets have train/dev/test splits per language. The dataset is cleaned up by page filtering to remove disambiguation pages, redirect pages, deleted pages, and non-entity pages. Each example contains the wikidata id of the entity, and the full Wikipedia article after page processing that removes non-content sections and structured objects.		null	8	708	--- language: - en paperswithcode_id: wiki-40b pretty_name: Wiki-40B dataset_info: features: - name: wikidata_id dtype: string - name: text dtype: string - name: version_id dtype: string config_name: en splits: - name: train num_bytes: 9423623904 num_examples: 2926536 - name: validation num_bytes: 527383016 num_examples: 163597 - name: test num_bytes: 522219464 num_examples: 162274 download_size: 0 dataset_size: 10473226384 --- # Dataset Card for "wiki40b" ## 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://research.google/pubs/pub49029/](https://research.google/pubs/pub49029/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - 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 downloaded dataset files: 0.00 MB - Size of the generated dataset: 10.47 GB - Total amount of disk used: 10.47 GB ### Dataset Summary Clean-up text for 40+ Wikipedia languages editions of pages correspond to entities. The datasets have train/dev/test splits per language. The dataset is cleaned up by page filtering to remove disambiguation pages, redirect pages, deleted pages, and non-entity pages. Each example contains the wikidata id of the entity, and the full Wikipedia article after page processing that removes non-content sections and structured objects. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### en - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 10.47 GB - Total amount of disk used: 10.47 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### en - `wikidata_id`: a `string` feature. - `text`: a `string` feature. - `version_id`: a `string` feature. ### Data Splits \|name\| train \|validation\| test \| \|----\|------:\|---------:\|-----:\| \|en \|2926536\| 163597\|162274\| ## 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 ``` ``` ### Contributions Thanks to [@jplu](https://github.com/jplu), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
Yijia-Xiao/pii-medical_flashcards	2023-09-12T22:24:20.000Z	[ "region:us" ]	Yijia-Xiao	null	null	null	1	706	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string - name: cleaned_output dtype: string splits: - name: train num_bytes: 28620193 num_examples: 33955 download_size: 12411702 dataset_size: 28620193 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "pii-medical_flashcards" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/00dbfb2c	2023-09-17T14:41:51.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	706	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 240 num_examples: 10 download_size: 1450 dataset_size: 240 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "00dbfb2c" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wiki_lingua	2023-06-16T14:39:41.000Z	[ "task_categories:summarization", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "language:cs", "language:de", "language:en", "language:es", "language:fr", "language:hi", "language:id", "language:it", "language:ja", "language:ko", "language:nl", "language:pt", "language:ru", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-3.0", "arxiv:2010.03093", "region:us" ]	null	WikiLingua is a large-scale multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages was done by aligning the images that are used to describe each how-to step in an article.	@inproceedings{ladhak-etal-2020-wikilingua, title = "{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization", author = "Ladhak, Faisal and Durmus, Esin and Cardie, Claire and McKeown, Kathleen", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.findings-emnlp.360", doi = "10.18653/v1/2020.findings-emnlp.360", pages = "4034--4048", }	null	23	704	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar - cs - de - en - es - fr - hi - id - it - ja - ko - nl - pt - ru - th - tr - vi - zh license: - cc-by-3.0 multilinguality: - multilingual size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: wikilingua pretty_name: WikiLingua dataset_info: - config_name: arabic features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 119116119 num_examples: 9995 download_size: 119358890 dataset_size: 119116119 - config_name: chinese features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 41170689 num_examples: 6541 download_size: 41345464 dataset_size: 41170689 - config_name: czech features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 20816390 num_examples: 2520 download_size: 20894511 dataset_size: 20816390 - config_name: dutch features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 87258040 num_examples: 10862 download_size: 87533442 dataset_size: 87258040 - config_name: english features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string splits: - name: train num_bytes: 333700114 num_examples: 57945 download_size: 338036185 dataset_size: 333700114 - config_name: french features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 197550376 num_examples: 21690 download_size: 198114157 dataset_size: 197550376 - config_name: german features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 168674340 num_examples: 20103 download_size: 169195050 dataset_size: 168674340 - config_name: hindi features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 63785051 num_examples: 3402 download_size: 63874759 dataset_size: 63785051 - config_name: indonesian features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 136408861 num_examples: 16308 download_size: 136833587 dataset_size: 136408861 - config_name: italian features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 138119527 num_examples: 17673 download_size: 138578956 dataset_size: 138119527 - config_name: japanese features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 40145031 num_examples: 4372 download_size: 40259570 dataset_size: 40145031 - config_name: korean features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 38647614 num_examples: 4111 download_size: 38748961 dataset_size: 38647614 - config_name: portuguese features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 204270845 num_examples: 28143 download_size: 204997686 dataset_size: 204270845 - config_name: russian features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 241924032 num_examples: 18143 download_size: 242377242 dataset_size: 241924032 - config_name: spanish features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 314618618 num_examples: 38795 download_size: 315609530 dataset_size: 314618618 - config_name: thai features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 86982851 num_examples: 5093 download_size: 87104200 dataset_size: 86982851 - config_name: turkish features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 11371821 num_examples: 1512 download_size: 11405793 dataset_size: 11371821 - config_name: vietnamese features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 69868788 num_examples: 6616 download_size: 70024093 dataset_size: 69868788 config_names: - arabic - chinese - czech - dutch - english - french - german - hindi - indonesian - italian - japanese - korean - portuguese - russian - spanish - thai - turkish - vietnamese --- # Dataset Card for "wiki_lingua" ## 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: [URL](https://github.com/esdurmus/Wikilingua) - Paper: [WikiLingua: A Multilingual Abstractive Summarization Dataset](https://arxiv.org/abs/2010.03093) ### Dataset Summary We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The table below shows number of article-summary pairs with a parallel article-summary pair in English. ______________________________ \| Language \| Num. parallel \| \| ----------- \| --------------\| \| English \| 141,457 \| \| Spanish \| 113,215 \| \| Portuguese \| 81,695 \| \| French \| 63,692 \| \| German \| 58,375 \| \| Russian \| 52,928 \| \| Italian \| 50,968 \| \| Indonesian \| 47,511 \| \| Dutch \| 31,270 \| \| Arabic \| 29,229 \| \| Vietnamese \| 19,600 \| \| Chinese \| 18,887 \| \| Thai \| 14,770 \| \| Japanese \| 12,669 \| \| Korean \| 12,189 \| \| Hindi \| 9,929 \| \| Czech \| 7,200 \| \| Turkish \| 4,503 \| ## Dataset Structure ### Data Instances ``` { 'article': { 'document': ['make sure that the area is a safe place, especially if you plan on walking home at night. It’s always a good idea to practice the buddy system. Have a friend meet up and walk with you. Research the bus, train, or streetcar routes available in your area to find safe and affordable travel to your destination. Make sure you check the schedule for your outgoing and return travel. Some public transportation will cease to run late at night. Be sure if you take public transportation to the venue that you will also be able to get home late at night. Check the routes. Even if some public transit is still running late at night, the routing may change. Some may run express past many of the stops, or not travel all the way to the ends. Be sure that your stop will still be available when you need it for your return trip. If you are taking public transit in a vulnerable state after drinking, it is always a good idea to travel in groups. Having friends available is a good way to stay safe and make sure that you reach your destination. This is more expensive option than a taxi or ride share service, but could be a fun and fancy way to stay safe and ensure that you will have a ride home. Plan this service in advance with a scheduled time to pick you up from your home and the venue. You want to be sure that the service will still be available when you need to get home. This may be easy in a large city, but taxis may be less frequent in smaller towns. This is especially true late at night, so this is a less reliable option than scheduling a ride in advance. Have a friend accompany you and help you flag a cab to make sure you are able to get one. Set up a plan to call a friend when you get home to make sure that you made it safely to your destination. If there are no taxis readily available call a local service to send a car to pick you up. You can share a ride with your friends, or other people using the app at the same moment. If you are in a vulnerable state it is best to share the ride with your friends to make sure you get home safe. You can request the car to yourself rather than sharing rides with strangers. If you travel home on your own or are the last of your group to be dropped off, make plans to call a friend when you get home so they know you made it safely to your destination. There may be a designated driver service in your area which can chauffeur your group. Make reservations with them in advance and keep their contact information handy while you are drinking.', "Designating a driver is a very popular tactic to avoid drinking and driving. It is important to plan in advance, because your brain function will slow down and your decision making skills will be impaired once you start drinking. Decide before you begin drinking that you will not drive. Figure out who will be getting you home before you leave. Make sure this person is responsible and keep them in your sight while you are drinking. Have their contact information handy in case you can’t find them when you are ready to leave. Choose a friend who doesn’t drink alcohol. You likely have someone in your friend group who doesn’t drink. This person is the most likely to remain sober. Decide on one person who will remain sober. You can take turns within your friend group, alternating who will be the designated driver on each occasion. Be sure that the designated driver actually remains sober. The person who has drank the least is still not sober. If you don’t have your car with you, you can guarantee that you won’t make the choice to drive it home. If you are drinking at your home. Give your keys to a responsible friend to ensure that you don't choose to drive somewhere after you have been drinking. It may be tempting to stay longer or leave with someone else. Stick to the plan you made in advance and only leave with your sober, designated driver. Keep the phone number of your driver handy in case you can't find them when you are ready to leave. If your designated driver drinks alcohol, find alternate transportation to get home.", 'If you have been drinking at all you are at least on the spectrum of drunkenness. You could be showing signs of impairment and slower brain function including lack of motor skills and slower reaction time, leading to the inability to operate a motor vehicle. Some of these signs could be: Poor balance or stumbling. Difficulty speaking clearly and slurred words. Abnormal behavior leading to you doing things you wouldn’t normally do if you were sober. As soon as you notice that you are showing signs of impairment, give your keys to a friend, the host or the bartender to ensure that you won’t drive until you are sober. Make sure to only give them your car key. Hold onto your house keys. If your friend, the host or the bartender are advising you not to drive, you are likely too drunk. Listen to their advice and acknowledge that they are trying to help you. Bystander intervention is common when it comes to drinking and driving. Many people will be willing to step in, take your keys and help you get home safely. If no one if offering to help, you may need to ask. Take a ride from a sober friend. It is best to get in a car with someone you trust when you are in this vulnerable state. Allow the host or bartender to call a cab or car service to take you home. If you are having a difficult time finding a safe way to get home, find a place to stay which does not involve you driving. Ask the host of the party if there is a place you can sleep. Give them your keys and ask that they keep them in a safe place until the morning. Stay with a friend if they live nearby and are on their way home. Find a hotel within walking distance. Call them to book a room, or have a friend help you secure one. Ask the friend if they will walk you to the hotel and make sure you get checked in safely. There are people in your life who care about you and want to be sure that you are safe. It may seem scary or embarrassing to call your parents or your siblings if you are too drunk to drive, but they will be glad you did. Your safety is the most important. You may need your phone to call someone for a ride or get help from a friend. Be sure to charge your phone before you leave the house. It is also a good idea to bring a charger with you in case your battery dies before the end of the night or you end up staying where you are and need to get home the next morning. You may also want to invest in a portable battery charger for your phone should there not be a power outlet available. Make sure it is fully charged before you leave your house. Keep it handy in your pocket or your bag throughout the night.' ], 'section_name': ['Finding Other Transportation', 'Designating a Driver', 'Staying Safe' ], 'summary': ['Walk to the venue where you will be drinking if it is close enough. Take public transit. Show up in style by hiring a limo or black car service. Flag a taxi cab for a convenient option to get where you’re going. Request a rideshare service like Uber or Lyft using an app on your phone. Reserve a designated driver service.', 'Plan in advance. Assign a designated driver. Leave your car at home. Leave the venue with your designated driver.', 'Pay attention to your body. Give up your keys. Listen to other people. Accept help. Stay where you are. Have an emergency back-up plan. Make sure that your phone is charged.' ] }, 'url': 'https://www.wikihow.com/Avoid-Drinking-and-Driving' } ``` ### Data Fields - `url`: WikiHow URL of the article - `article`: A dictionary containing `section_name`, `document` and `summary` - `section_name`: List of section headings in an article - `document`: List of documents, one for each section in the `section_name` list - `summary`: List of summarized document ### Data Splits \| \| train \| \|:-----------\|--------:\| \| arabic \| 9995 \| \| chinese \| 6541 \| \| czech \| 2520 \| \| dutch \| 10862 \| \| english \| 57945 \| \| french \| 21690 \| \| german \| 20103 \| \| hindi \| 3402 \| \| indonesian \| 16308 \| \| italian \| 17673 \| \| japanese \| 4372 \| \| korean \| 4111 \| \| portuguese \| 28143 \| \| russian \| 18143 \| \| spanish \| 6616 \| \| thai \| 5093 \| \| turkish \| 1512 \| \| vietnamese \| 6616 \| ## 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 - Article provided by wikiHow https://www.wikihow.com/Main-Page, a wiki building the world's largest, highest quality how-to manual. Please edit this article and find author credits at wikiHow.com. Content on wikiHow can be shared under a [Creative Commons license](http://creativecommons.org/licenses/by-nc-sa/3.0/). - Refer to [this webpage](https://www.wikihow.com/wikiHow:Attribution) for the specific attribution guidelines. - also see https://gem-benchmark.com/data_cards/WikiLingua ### Citation Information ```bibtex @inproceedings{ladhak-etal-2020-wikilingua, title = "{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization", author = "Ladhak, Faisal and Durmus, Esin and Cardie, Claire and McKeown, Kathleen", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.findings-emnlp.360", doi = "10.18653/v1/2020.findings-emnlp.360", pages = "4034--4048", } ``` ### Contributions Thanks to [@katnoria](https://github.com/katnoria) for adding this dataset.
nomic-ai/gpt4all-j-prompt-generations	2023-04-24T15:20:43.000Z	[ "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "region:us" ]	nomic-ai	null	null	null	159	699	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 1774285641 num_examples: 808812 download_size: 990673616 dataset_size: 1774285641 license: apache-2.0 language: - en size_categories: - 100K<n<1M --- # Dataset Card for [GPT4All-J Prompt Generations] ## Dataset Description Dataset used to train [GPT4All-J](https://huggingface.co/nomic-ai/gpt4all-j) and [GPT4All-J-LoRA](https://huggingface.co/nomic-ai/gpt4all-j-lora) We release several versions of datasets - v1.0: The original dataset we used to finetune GPT-J on - v1.1-breezy: A filtered dataset where we removed all instances of `AI language model` - v1.2-jazzy: A filtered dataset where we also removed instances like `I'm sorry, I can't answer...` and `AI language model` - v1.3-groovy: The v1.2 dataset with ShareGPT and Dolly added with ~8% of semantic duplicates removed from the dataset using [Atlas](https://atlas.nomic.ai/) The dataset defaults to `main` which is `v1.0`. To download a specific version, you can pass an argument to the keyword `revision` in `load_dataset`: ```python from datasets import load_dataset jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy') ``` - Homepage: [gpt4all.io](https://gpt4all.io/) - Repository: [gpt4all](https://github.com/nomic-ai/gpt4all) - Paper: [Technical Report](https://static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf) - Atlas Map: [Map of Prompts](https://atlas.nomic.ai/map/gpt4all-j-prompts-curated) and [Responses](https://atlas.nomic.ai/map/gpt4all-j-response-curated)
result-kand2-sdxl-wuerst-karlo/8edb1fe9	2023-09-18T04:48:26.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	697	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 258 num_examples: 10 download_size: 1429 dataset_size: 258 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "8edb1fe9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tilyupo/trivia_qa	2023-08-03T17:00:54.000Z	[ "region:us" ]	tilyupo	null	null	null	0	696	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: question dtype: string - name: question_id dtype: string - name: question_source dtype: string - name: answer struct: - name: aliases sequence: string - name: normalized_aliases sequence: string - name: matched_wiki_entity_name dtype: string - name: normalized_matched_wiki_entity_name dtype: string - name: normalized_value dtype: string - name: type dtype: string - name: value dtype: string - name: passages list: - name: answer dtype: string - name: passage dtype: string - name: precise_score dtype: float64 - name: rough_score dtype: float64 - name: source dtype: string - name: title dtype: string splits: - name: train num_bytes: 3065861634 num_examples: 137282 - name: validation num_bytes: 402091161 num_examples: 17817 download_size: 1805238996 dataset_size: 3467952795 --- # Dataset Card for "trivia_qa_passages" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
e2e_nlg_cleaned	2022-11-18T19:59:46.000Z	[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "meaning-representation-to-text", "arxiv:1706.09254", "arxiv:1901.11528", "region:us" ]	null	An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper: Ondřej Dušek, David M. Howcroft, and Verena Rieser (2019): Semantic Noise Matters for Neural Natural Language Generation. In INLG, Tokyo, Japan.	@inproceedings{dusek-etal-2019-semantic, title = "Semantic Noise Matters for Neural Natural Language Generation", author = "Du{\v{s}}ek, Ond{\v{r}}ej and Howcroft, David M. and Rieser, Verena", booktitle = "Proceedings of the 12th International Conference on Natural Language Generation", month = oct # "{--}" # nov, year = "2019", address = "Tokyo, Japan", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-8652", doi = "10.18653/v1/W19-8652", pages = "421--426" }	null	3	695	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: null pretty_name: the Cleaned Version of the E2E Dataset tags: - meaning-representation-to-text dataset_info: features: - name: meaning_representation dtype: string - name: human_reference dtype: string splits: - name: train num_bytes: 7474936 num_examples: 33525 - name: validation num_bytes: 1056527 num_examples: 4299 - name: test num_bytes: 1262597 num_examples: 4693 download_size: 14597407 dataset_size: 9794060 --- # Dataset Card for the Cleaned Version of the E2E 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: [homepage](http://www.macs.hw.ac.uk/InteractionLab/E2E/) - Repository: [repository](https://github.com/tuetschek/e2e-dataset/) - Paper: [paper](https://arxiv.org/abs/1706.09254) - Leaderboard: [leaderboard](http://www.macs.hw.ac.uk/InteractionLab/E2E/) ### Dataset Summary An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper: The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. E2E is released in the following paper where you can find more details and baseline results: https://arxiv.org/abs/1706.09254 ### Supported Tasks and Leaderboards - `text2text-generation-other-meaning-representtion-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a high [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider). This task has an inactive leaderboard which can be found [here](http://www.macs.hw.ac.uk/InteractionLab/E2E/) and ranks models based on the metrics above. ### Languages The dataset is in english (en). ## Dataset Structure ### Data Instances Example of one instance: ``` {'human_reference': 'The Vaults pub near Café Adriatic has a 5 star rating. Prices start at £30.', 'meaning_representation': 'name[The Vaults], eatType[pub], priceRange[more than £30], customer rating[5 out of 5], near[Café Adriatic]'} ``` ### Data Fields - `human_reference`: string, the text is natural language that describes the different characteristics in the meaning representation - `meaning_representation`: list of slots and values to generate a description from Each MR consists of 3–8 attributes (slots), such as name, food or area, and their values. ### Data Splits The dataset is split into training, validation and testing sets (in a 76.5-8.5-15 ratio), keeping a similar distribution of MR and reference text lengths and ensuring that MRs in different sets are distinct. \| \| train \| validation \| test \| \|--------------\|------:\|-----------:\|-----:\| \| N. Instances \| 33525 \| 4299 \| 4693 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization The data was collected using the CrowdFlower platform and quality-controlled following Novikova et al. (2016). #### Who are the source language producers? [More Information Needed] ### Annotations Following Novikova et al. (2016), the E2E data was collected using pictures as stimuli, which was shown to elicit significantly more natural, more informative, and better phrased human references than textual MRs. #### 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 ``` @article{dusek.etal2020:csl, title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}}, author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena}, year = {2020}, month = jan, volume = {59}, pages = {123--156}, doi = {10.1016/j.csl.2019.06.009}, archivePrefix = {arXiv}, eprint = {1901.11528}, eprinttype = {arxiv}, journal = {Computer Speech \& Language} ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
Multimodal-Fatima/SNLI-VE_test	2023-02-07T22:33:34.000Z	[ "region:us" ]	Multimodal-Fatima	null	null	null	0	694	--- dataset_info: features: - name: image dtype: image - name: filename dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: id dtype: int64 - name: id_image dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string splits: - name: test num_bytes: 2483209080.488 num_examples: 17901 download_size: 911606574 dataset_size: 2483209080.488 --- # Dataset Card for "SNLI-VE_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ChristophSchuhmann/improved_aesthetics_6.5plus	2022-08-10T11:34:17.000Z	[ "region:us" ]	ChristophSchuhmann	null	null	null	32	688	Entry not found
mteb/imdb	2022-09-27T19:14:44.000Z	[ "language:en", "region:us" ]	mteb	null	null	null	1	687	--- language: - en ---
BeIR/trec-covid	2022-10-23T06:00:45.000Z	[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]	BeIR	null	null	null	0	684	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## 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 [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
Amod/mental_health_counseling_conversations	2023-07-20T19:00:46.000Z	[ "task_categories:conversational", "task_categories:text-generation", "task_categories:question-answering", "task_ids:sentiment-classification", "task_ids:language-modeling", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:openrail", "region:us" ]	Amod	null	null	null	26	683	--- annotations_creators: - no-annotation language_creators: - found language: - en license: openrail multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational - text-generation - question-answering task_ids: - sentiment-classification - language-modeling - open-domain-qa --- # Amod/mental_health_counseling_conversations ## 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-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) ## Dataset Description - Homepage: - Repository: - Paper: Bertagnolli, Nicolas (2020). Counsel chat: Bootstrapping high-quality therapy data. Towards Data Science. https://towardsdatascience.com/counsel-chat - Leaderboard: - Point of Contact: ### Dataset Summary This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice. ### Supported Tasks and Leaderboards The dataset supports the task of text generation, particularly for generating advice or suggestions in response to a mental health-related question. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances A data instance includes a 'Context' and a 'Response'. 'Context' contains the question asked by a user, and 'Response' contains the corresponding answer provided by a psychologist. ### Data Fields - 'Context': a string containing the question asked by a user - 'Response': a string containing the corresponding answer provided by a psychologist ### Data Splits The dataset has no predefined splits. Users can create their own splits as needed. ## Dataset Creation ### Curation Rationale This dataset was created to aid in the development of AI models that can provide mental health advice or guidance. The raw data was meticulously cleaned to only include the conversations. ### Source Data The data was sourced from two online counseling and therapy platforms. The raw data can be found [here](https://github.com/nbertagnolli/counsel-chat/tree/master/data). ### Annotations The dataset does not contain any additional annotations. ### Personal and Sensitive Information The dataset may contain sensitive information related to mental health. All data was anonymized and no personally identifiable information is included.
result-kand2-sdxl-wuerst-karlo/b2489367	2023-09-18T15:20:18.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	683	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 254 num_examples: 10 download_size: 1431 dataset_size: 254 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b2489367" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EleutherAI/hendrycks_ethics	2023-07-05T21:23:28.000Z	[ "region:us" ]	EleutherAI	The ETHICS dataset is a benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents.	@article{hendrycks2021ethics title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} }	null	0	682	Entry not found
result-kand2-sdxl-wuerst-karlo/4e6d4d01	2023-09-18T17:02:52.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	682	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 176 num_examples: 10 download_size: 1328 dataset_size: 176 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "4e6d4d01" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/9bc865b4	2023-09-18T17:00:59.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	681	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1354 dataset_size: 188 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "9bc865b4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/b6ea8c05	2023-09-18T17:02:55.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	681	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 176 num_examples: 10 download_size: 1328 dataset_size: 176 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b6ea8c05" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
movie_rationales	2023-04-05T10:09:59.000Z	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]	null	The movie rationale dataset contains human annotated rationales for movie reviews.	@unpublished{eraser2019, title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models}, author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace} } @InProceedings{zaidan-eisner-piatko-2008:nips, author = {Omar F. Zaidan and Jason Eisner and Christine Piatko}, title = {Machine Learning with Annotator Rationales to Reduce Annotation Cost}, booktitle = {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning}, month = {December}, year = {2008} }	null	2	680	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: MovieRationales dataset_info: features: - name: review dtype: string - name: label dtype: class_label: names: '0': NEG '1': POS - name: evidences sequence: string splits: - name: test num_bytes: 1046377 num_examples: 199 - name: train num_bytes: 6853624 num_examples: 1600 - name: validation num_bytes: 830417 num_examples: 200 download_size: 3899487 dataset_size: 8730418 --- # Dataset Card for "movie_rationales" ## 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/jayded/eraserbenchmark - Paper: [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://aclanthology.org/2020.acl-main.408/) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 3.90 MB - Size of the generated dataset: 8.73 MB - Total amount of disk used: 12.62 MB ### Dataset Summary The movie rationale dataset contains human annotated rationales for movie reviews. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 3.90 MB - Size of the generated dataset: 8.73 MB - Total amount of disk used: 12.62 MB An example of 'validation' looks as follows. ``` { "evidences": ["Fun movie"], "label": 1, "review": "Fun movie\n" } ``` ### Data Fields The data fields are the same among all splits. #### default - `review`: a `string` feature. - `label`: a classification label, with possible values including `NEG` (0), `POS` (1). - `evidences`: a `list` of `string` features. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\| 1600\| 200\| 199\| ## 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{deyoung-etal-2020-eraser, title = "{ERASER}: {A} Benchmark to Evaluate Rationalized {NLP} Models", author = "DeYoung, Jay and Jain, Sarthak and Rajani, Nazneen Fatema and Lehman, Eric and Xiong, Caiming and Socher, Richard and Wallace, Byron C.", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.acl-main.408", doi = "10.18653/v1/2020.acl-main.408", pages = "4443--4458", } @InProceedings{zaidan-eisner-piatko-2008:nips, author = {Omar F. Zaidan and Jason Eisner and Christine Piatko}, title = {Machine Learning with Annotator Rationales to Reduce Annotation Cost}, booktitle = {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning}, month = {December}, year = {2008} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
nickrosh/Evol-Instruct-Code-80k-v1	2023-07-11T02:05:26.000Z	[ "license:cc-by-nc-sa-4.0", "arxiv:2306.08568", "region:us" ]	nickrosh	null	null	null	83	678	--- license: cc-by-nc-sa-4.0 --- Open Source Implementation of Evol-Instruct-Code as described in the [WizardCoder Paper](https://arxiv.org/pdf/2306.08568.pdf). Code for the intruction generation can be found on Github as [Evol-Teacher](https://github.com/nickrosh/evol-teacher).
lmqg/qg_squad	2022-12-02T18:51:10.000Z	[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:squad", "language:en", "license:cc-by-4.0", "question-generation", "arxiv:2210.03992", "arxiv:1705.00106", "region:us" ]	lmqg	[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) evaluation set for the question generation (QG) models. The split of test and development set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11).	@inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", }	null	4	675	--- license: cc-by-4.0 pretty_name: SQuAD for question generation language: en multilinguality: monolingual size_categories: 10K<n<100K source_datasets: squad task_categories: - text-generation task_ids: - language-modeling tags: - question-generation --- # Dataset Card for "lmqg/qg_squad" ## Dataset Description - Repository: [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - Paper: [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - Point of Contact: [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992). This is [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset for question generation (QG) task. The split of train/development/test set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11). ### Supported Tasks and Leaderboards * `question-generation`: The dataset is assumed to be used to train a model for question generation. Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). This task has an active leaderboard which can be found at [here](https://paperswithcode.com/sota/question-generation-on-squad11). ### Languages English (en) ## Dataset Structure An example of 'train' looks as follows. ``` { "question": "What is heresy mainly at odds with?", "paragraph": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "answer": "established beliefs or customs", "sentence": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs .", "paragraph_sentence": "<hl> Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs . <hl> A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "paragraph_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl>. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "sentence_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl> ." } ``` The data fields are the same among all splits. - `question`: a `string` feature. - `paragraph`: a `string` feature. - `answer`: a `string` feature. - `sentence`: a `string` feature. - `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`. - `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`. - `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`. Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model, but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and `paragraph_sentence` feature is for sentence-aware question generation. ## Data Splits \|train\|validation\|test \| \|----:\|---------:\|----:\| \|75722\| 10570\|11877\| ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
mteb/twentynewsgroups-clustering	2022-09-27T19:13:51.000Z	[ "language:en", "region:us" ]	mteb	null	null	null	0	671	--- language: - en ---
kmfoda/booksum	2022-11-30T12:03:43.000Z	[ "license:bsd-3-clause", "arxiv:2105.08209", "region:us" ]	kmfoda	null	null	null	25	670	--- license: - bsd-3-clause train-eval-index: - config: kmfoda--booksum task: summarization task_id: summarization splits: eval_split: test col_mapping: chapter: text summary_text: target --- # BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization Authors: [Wojciech Kryściński](https://twitter.com/iam_wkr), [Nazneen Rajani](https://twitter.com/nazneenrajani), [Divyansh Agarwal](https://twitter.com/jigsaw2212), [Caiming Xiong](https://twitter.com/caimingxiong), [Dragomir Radev](http://www.cs.yale.edu/homes/radev/) ## Introduction The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset. ## Links - [paper](https://arxiv.org/abs/2105.08209) by SalesForce Research - [GitHub repo](https://github.com/salesforce/booksum) <p align="center"><img src="misc/book_sumv4.png"></p> ## Table of Contents 1. [Citation](#citation) 2. [Legal Note](#legal-note) 3. [License](#license) ## Citation ``` @article{kryscinski2021booksum, title={BookSum: A Collection of Datasets for Long-form Narrative Summarization}, author={Wojciech Kry{\'s}ci{\'n}ski and Nazneen Rajani and Divyansh Agarwal and Caiming Xiong and Dragomir Radev}, year={2021}, eprint={2105.08209}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Legal Note By downloading or using the resources, including any code or scripts, shared in this code repository, you hereby agree to the following terms, and your use of the resources is conditioned on and subject to these terms. 1. You may only use the scripts shared in this code repository for research purposes. You may not use or allow others to use the scripts for any other purposes and other uses are expressly prohibited. 2. You will comply with all terms and conditions, and are responsible for obtaining all rights, related to the services you access and the data you collect. 3. We do not make any representations or warranties whatsoever regarding the sources from which data is collected. Furthermore, we are not liable for any damage, loss or expense of any kind arising from or relating to your use of the resources shared in this code repository or the data collected, regardless of whether such liability is based in tort, contract or otherwise. ## License The code is released under the BSD-3 License (see `LICENSE.txt` for details).
ceyda/smithsonian_butterflies	2022-07-13T09:32:27.000Z	[ "task_categories:image-classification", "task_ids:multi-label-image-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc0-1.0", "region:us" ]	ceyda	null	null	null	6	670	--- annotations_creators: - expert-generated language: - en language_creators: - expert-generated license: - cc0-1.0 multilinguality: - monolingual pretty_name: Smithsonian Butterflies size_categories: - n<1K source_datasets: - original task_categories: - image-classification task_ids: - multi-label-image-classification --- # Dataset Card for [Smithsonian Butterflies] ## 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: Smithsonian "Education and Outreach" & "NMNH - Entomology Dept." collections [here](https://collections.si.edu/search/results.htm?q=butterfly&view=list&fq=online_media_type%3A%22Images%22&fq=topic%3A%22Insects%22&fq=data_source%3A%22NMNH+-+Entomology+Dept.%22&media.CC0=true&dsort=title&start=0) ### Dataset Summary High-res images from Smithsonian "Education and Outreach" & "NMNH - Entomology Dept." collections. Crawled ### Supported Tasks and Leaderboards Includes metadata about the scientific name of butterflies, but there maybe missing values. Might be good for classification. ### Languages English ## Dataset Structure ### Data Instances # Example data ``` {'image_url': 'https://ids.si.edu/ids/deliveryService?id=ark:/65665/m3b3132f6666904de396880d9dc811c5cd', 'image_alt': 'view Aholibah Underwing digital asset number 1', 'id': 'ark:/65665/m3b3132f6666904de396880d9dc811c5cd', 'name': 'Aholibah Underwing', 'scientific_name': 'Catocala aholibah', 'gender': None, 'taxonomy': 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Noctuidae, Catocalinae', 'region': None, 'locality': None, 'date': None, 'usnm_no': 'EO400317-DSP', 'guid': 'http://n2t.net/ark:/65665/39b506292-715f-45a7-8511-b49bb087c7de', 'edan_url': 'edanmdm:nmnheducation_10866595', 'source': 'Smithsonian Education and Outreach collections', 'stage': None, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2000x1328 at 0x7F57D0504DC0>, 'image_hash': '27a5fe92f72f8b116d3b7d65bac84958', 'sim_score': 0.8440760970115662} ``` ### Data Fields sim-score indicates clip score for "pretty butterfly". This is to eliminate non-butterfly images(just id card images etc) ### Data Splits No specific split exists. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] Crawled from "Education and Outreach" & "NMNH - Entomology Dept." collections found online [here](https://collections.si.edu/search/results.htm?q=butterfly&view=list&fq=online_media_type%3A%22Images%22&fq=topic%3A%22Insects%22&fq=data_source%3A%22NMNH+-+Entomology+Dept.%22&media.CC0=true&dsort=title&start=0) #### 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 Doesn't include all butterfly species ## Additional Information ### Dataset Curators Smithsonian "Education and Outreach" & "NMNH - Entomology Dept." collections ### Licensing Information Only results marked: CC0 ### Citation Information [More Information Needed] ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
juletxara/xcopa_mt	2023-07-21T10:19:22.000Z	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended\|copa", "language:en", "license:cc-by-4.0", "region:us" ]	juletxara	XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the creation of XCOPA and the implementation of the baselines are available in the paper.\n	@article{ponti2020xcopa, title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning}, author={Edoardo M. Ponti, Goran Glava\v{s}, Olga Majewska, Qianchu Liu, Ivan Vuli\'{c} and Anna Korhonen}, journal={arXiv preprint}, year={2020}, url={https://ducdauge.github.io/files/xcopa.pdf} } @inproceedings{roemmele2011choice, title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning}, author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S}, booktitle={2011 AAAI Spring Symposium Series}, year={2011}, url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF}, }	null	0	670	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: XCOPA MT size_categories: - unknown source_datasets: - extended\|copa task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: xcopa dataset_info: - config_name: nllb-200-distilled-600M features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - 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name: tr num_bytes: 55743 num_examples: 500 - name: vi num_bytes: 56371 num_examples: 500 download_size: 1015352 dataset_size: 648791 - config_name: RedPajama-INCITE-Base-3B-v1 features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 66862 num_examples: 500 - name: ht num_bytes: 67548 num_examples: 500 - name: it num_bytes: 60220 num_examples: 500 - name: id num_bytes: 58585 num_examples: 500 - name: qu num_bytes: 84898 num_examples: 500 - name: sw num_bytes: 78422 num_examples: 500 - name: zh num_bytes: 60708 num_examples: 500 - name: ta num_bytes: 99438 num_examples: 500 - name: th num_bytes: 83022 num_examples: 500 - name: tr num_bytes: 64835 num_examples: 500 - name: vi num_bytes: 68696 num_examples: 500 download_size: 1161592 dataset_size: 793234 - config_name: RedPajama-INCITE-7B-Base features: - name: premise dtype: string - 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config_name: open_llama_7b_v2 features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 53471 num_examples: 500 - name: ht num_bytes: 55430 num_examples: 500 - name: it num_bytes: 59523 num_examples: 500 - name: id num_bytes: 57590 num_examples: 500 - name: qu num_bytes: 87887 num_examples: 500 - name: sw num_bytes: 62658 num_examples: 500 - name: zh num_bytes: 57696 num_examples: 500 - name: ta num_bytes: 94160 num_examples: 500 - name: th num_bytes: 58255 num_examples: 500 - name: tr num_bytes: 54985 num_examples: 500 - name: vi num_bytes: 57207 num_examples: 500 download_size: 1066611 dataset_size: 698862 - config_name: falcon-7b features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - 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name: tr num_bytes: 56341 num_examples: 500 - name: vi num_bytes: 58305 num_examples: 500 download_size: 1003224 dataset_size: 636294 - config_name: xgen-7b-8k-inst features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 57938 num_examples: 500 - name: ht num_bytes: 59577 num_examples: 500 - name: it num_bytes: 58999 num_examples: 500 - name: id num_bytes: 57198 num_examples: 500 - name: qu num_bytes: 74792 num_examples: 500 - name: sw num_bytes: 63739 num_examples: 500 - name: zh num_bytes: 58638 num_examples: 500 - name: ta num_bytes: 14813 num_examples: 500 - name: th num_bytes: 64762 num_examples: 500 - name: tr num_bytes: 58008 num_examples: 500 - name: vi num_bytes: 56758 num_examples: 500 download_size: 992574 dataset_size: 625222 - config_name: polylm-1.7b features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 127291 num_examples: 500 - name: ht num_bytes: 100114 num_examples: 500 - name: it num_bytes: 70393 num_examples: 500 - name: id num_bytes: 58829 num_examples: 500 - name: qu num_bytes: 92265 num_examples: 500 - name: sw num_bytes: 88160 num_examples: 500 - name: zh num_bytes: 56896 num_examples: 500 - name: ta num_bytes: 123071 num_examples: 500 - name: th num_bytes: 67106 num_examples: 500 - name: tr num_bytes: 107151 num_examples: 500 - name: vi num_bytes: 56025 num_examples: 500 download_size: 1326335 dataset_size: 947301 - config_name: polylm-13b features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 52813 num_examples: 500 - 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config_name: Llama-2-7b-hf features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 55987 num_examples: 500 - name: ht num_bytes: 55689 num_examples: 500 - name: it num_bytes: 59478 num_examples: 500 - name: id num_bytes: 58155 num_examples: 500 - name: qu num_bytes: 64673 num_examples: 500 - name: sw num_bytes: 59586 num_examples: 500 - name: zh num_bytes: 57100 num_examples: 500 - name: ta num_bytes: 84633 num_examples: 500 - name: th num_bytes: 55732 num_examples: 500 - name: tr num_bytes: 55864 num_examples: 500 - name: vi num_bytes: 55716 num_examples: 500 download_size: 1029561 dataset_size: 662613 - config_name: Llama-2-13b-hf features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 57638 num_examples: 500 - name: ht num_bytes: 58376 num_examples: 500 - name: it num_bytes: 59731 num_examples: 500 - name: id num_bytes: 57842 num_examples: 500 - name: qu num_bytes: 67524 num_examples: 500 - name: sw num_bytes: 63141 num_examples: 500 - name: zh num_bytes: 57165 num_examples: 500 - name: ta num_bytes: 68926 num_examples: 500 - name: th num_bytes: 56742 num_examples: 500 - name: tr num_bytes: 56300 num_examples: 500 - name: vi num_bytes: 56077 num_examples: 500 download_size: 1026046 dataset_size: 659462 - config_name: Llama-2-7b-chat-hf features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 50593 num_examples: 500 - name: ht num_bytes: 64307 num_examples: 500 - name: it num_bytes: 25365 num_examples: 500 - name: id num_bytes: 51404 num_examples: 500 - name: qu num_bytes: 77738 num_examples: 500 - name: sw num_bytes: 64286 num_examples: 500 - name: zh num_bytes: 21421 num_examples: 500 - name: ta num_bytes: 80610 num_examples: 500 - name: th num_bytes: 66935 num_examples: 500 - name: tr num_bytes: 54474 num_examples: 500 - name: vi num_bytes: 28370 num_examples: 500 download_size: 952208 dataset_size: 585503 - config_name: Llama-2-13b-chat-hf features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: et num_bytes: 60368 num_examples: 500 - name: ht num_bytes: 65837 num_examples: 500 - name: it num_bytes: 59658 num_examples: 500 - name: id num_bytes: 59141 num_examples: 500 - name: qu num_bytes: 80708 num_examples: 500 - name: sw num_bytes: 66850 num_examples: 500 - name: zh num_bytes: 59536 num_examples: 500 - name: ta num_bytes: 91955 num_examples: 500 - name: th num_bytes: 65147 num_examples: 500 - name: tr num_bytes: 56932 num_examples: 500 - name: vi num_bytes: 57445 num_examples: 500 download_size: 1090195 dataset_size: 723577 --- # Dataset Card for XCOPA MT ## 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/cambridgeltl/xcopa](https://github.com/cambridgeltl/xcopa) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - 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 downloaded dataset files: 4.08 MB - Size of the generated dataset: 1.02 MB - Total amount of disk used: 5.10 MB ### Dataset Summary XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the creation of XCOPA and the implementation of the baselines are available in the paper. Xcopa language et ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages - et - ht - id - it - qu - sw - ta - th - tr - vi - zh ## Dataset Structure ### Data Instances #### et - Size of downloaded dataset files: 0.37 MB - Size of the generated dataset: 0.07 MB - Total amount of disk used: 0.44 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` #### ht - Size of downloaded dataset files: 0.37 MB - Size of the generated dataset: 0.07 MB - Total amount of disk used: 0.44 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` #### id - Size of downloaded dataset files: 0.37 MB - Size of the generated dataset: 0.07 MB - Total amount of disk used: 0.45 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` #### it - Size of downloaded dataset files: 0.37 MB - Size of the generated dataset: 0.08 MB - Total amount of disk used: 0.45 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` #### qu - Size of downloaded dataset files: 0.37 MB - Size of the generated dataset: 0.08 MB - Total amount of disk used: 0.45 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` ### Data Fields The data fields are the same among all splits. #### et - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. #### ht - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. #### id - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. #### it - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. #### qu - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. ### Data Splits \|name\|validation\|test\| \|----\|---------:\|---:\| \|et \| 100\| 500\| \|ht \| 100\| 500\| \|id \| 100\| 500\| \|it \| 100\| 500\| \|qu \| 100\| 500\| ## 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 [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @article{ponti2020xcopa, title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning}, author={Edoardo M. Ponti, Goran Glava {s}, Olga Majewska, Qianchu Liu, Ivan Vuli'{c} and Anna Korhonen}, journal={arXiv preprint}, year={2020}, url={https://ducdauge.github.io/files/xcopa.pdf} } @inproceedings{roemmele2011choice, title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning}, author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S}, booktitle={2011 AAAI Spring Symposium Series}, year={2011}, url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
result-kand2-sdxl-wuerst-karlo/908725e5	2023-09-19T00:19:24.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	669	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 161 num_examples: 10 download_size: 1318 dataset_size: 161 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "908725e5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
xiyuez/red-dot-design-award-product-description	2023-07-07T18:32:48.000Z	[ "task_categories:text-generation", "size_categories:10k<n<100K", "language:en", "license:odc-by", "region:us" ]	xiyuez	null	null	null	4	668	--- license: odc-by task_categories: - text-generation language: - en pretty_name: Red Dot Design Award Dataset size_categories: - 10k<n<100K --- # Red Dot Design Award Dataset This dataset contains information about the products that have won the Red Dot Design Award, a prestigious international design competition. The data was extracted from the official website of the award: <https://www.red-dot.org/>. ## Task The task for this dataset is text generation, specifically product description generation. Given a product name and category, the goal is to generate a concise and informative description that highlights the features and benefits of the product. ## Limitations The dataset may have some limitations, such as: - The data may contain false or outdated information, as it reflects the information available on the website at the time of extraction. - The data only covers the products that have won the award, which may introduce some selection bias or limit the diversity of the data. - The data is only in English, although the website also has a German version that could be crawled in the future. - The data does not include any images of the products, which could be useful for multimodal language models. Images are planned to be scraped in the future. ## License This public extract is licensed under the Open Data Commons Attribution License: <http://opendatacommons.org/licenses/by/1.0/>. ## Data Format The dataset consists of 21183 unique rows, each containing the following columns: - `product`: The name of the product that won the award. - `category`: The category of the product, such as "Video Camera", "Bathroom Shelf", or "Mobile Home". - `description`: A short paragraph describing the product, its features, and its benefits. There is no predefined train/test split for this dataset. Near-duplicates have been removed. ## Data Quality The data quality may vary depending on the source and accuracy of the information on the website. We have not verified, filtered, or modified the data in any way. The data may contain content that is toxic, biased, copyrighted, or false. Use of this dataset is at your own risk. We do not provide any warranties or liability. ## Acknowledgements We would like to acknowledge the Red Dot Design Award for hosting and maintaining the website that provided the data for this dataset. We do not claim any ownership or affiliation with the award or the website.
yair-elboher/text-toy	2023-10-06T09:35:55.000Z	[ "region:us" ]	yair-elboher	null	null	null	0	668	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 10849 num_examples: 9 - name: validation num_bytes: 8180 num_examples: 4 download_size: 30926 dataset_size: 19029 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "text-toy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GEM/e2e_nlg	2022-10-24T15:30:18.000Z	[ "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-4.0", "data-to-text", "region:us" ]	GEM	The E2E dataset is designed for a limited-domain data-to-text task -- generation of restaurant descriptions/recommendations based on up to 8 different attributes (name, area, price range etc.).	@inproceedings{e2e_cleaned, address = {Tokyo, Japan}, title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}}, url = {https://www.aclweb.org/anthology/W19-8652/}, booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)}, author = {Dušek, Ondřej and Howcroft, David M and Rieser, Verena}, year = {2019}, pages = {421--426}, }	null	2	667	--- annotations_creators: - none language_creators: - unknown language: - en license: - cc-by-sa-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: e2e_nlg tags: - data-to-text --- # Dataset Card for GEM/e2e_nlg ## Dataset Description - Homepage: http://www.macs.hw.ac.uk/InteractionLab/E2E/ - Repository: https://github.com/tuetschek/e2e-cleaning - Paper: https://www.aclweb.org/anthology/W17-5525/, [Detailed E2E Challenge writeup - Leaderboard: N/A - Point of Contact: Ondrej Dusek ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/e2e_nlg). ### Dataset Summary The E2E NLG dataset is an English benchmark dataset for data-to-text models that verbalize a set of 2-9 key-value attribute pairs in the restaurant domain. The version used for GEM is the cleaned E2E NLG dataset, which filters examples with hallucinations and outputs that don't fully cover all input attributes. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/e2e_nlg') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/e2e_nlg). #### website [Website](http://www.macs.hw.ac.uk/InteractionLab/E2E/) #### paper [First data release](https://www.aclweb.org/anthology/W17-5525/), [Detailed E2E Challenge writeup](https://doi.org/10.1016/j.csl.2019.06.009), [Cleaned E2E version](https://www.aclweb.org/anthology/W19-8652/) #### authors Jekaterina Novikova, Ondrej Dusek and Verena Rieser ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Website](http://www.macs.hw.ac.uk/InteractionLab/E2E/) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Github](https://github.com/tuetschek/e2e-cleaning) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [First data release](https://www.aclweb.org/anthology/W17-5525/), [Detailed E2E Challenge writeup](https://doi.org/10.1016/j.csl.2019.06.009), [Cleaned E2E version](https://www.aclweb.org/anthology/W19-8652/) #### 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{e2e_cleaned, address = {Tokyo, Japan}, title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}}, url = {https://www.aclweb.org/anthology/W19-8652/}, booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)}, author = {Dušek, Ondřej and Howcroft, David M and Rieser, Verena}, year = {2019}, pages = {421--426}, } ``` #### 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 --> Ondrej Dusek #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> odusek@ufal.mff.cuni.cz #### 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 Dialects <!-- info: What dialects are covered? Are there multiple dialects per language? --> <!-- scope: periscope --> Dialect-specific data was not collected and the language is general British English. #### 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 original dataset was collected using the CrowdFlower (now Appen) platform using native English speakers (self-reported). No demographic information was provided, but the collection was geographically limited to English-speaking countries. #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> The dataset was collected to test neural model on a very well specified realization task. #### 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 --> Producing a text informing/recommending a restaurant, given all and only the attributes specified on the input. ### 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 --> Heriot-Watt University #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Jekaterina Novikova, Ondrej Dusek and Verena Rieser #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> This research received funding from the EPSRC projects DILiGENt (EP/M005429/1) and MaDrIgAL (EP/N017536/1). #### 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 --> Simon Mille wrote the initial data card and Yacine Jernite the data loader. Sebastian Gehrmann migrated the data card to the v2 format and moved the data loader to the hub. ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> The data is in a CSV format, with the following fields: * `mr` -- the meaning representation (MR, input) * `ref` -- reference, i.e. the corresponding natural-language description (output) There are additional fields (`fixed`, `orig_mr`) indicating whether the data was modified in the cleaning process and what was the original MR before cleaning, but these aren't used for NLG. The MR has a flat structure -- attribute-value pairs are comma separated, with values enclosed in brackets (see example above). There are 8 attributes: * `name` -- restaurant name * `near` -- a landmark close to the restaurant * `area` -- location (riverside, city centre) * `food` -- food type / cuisine (e.g. Japanese, Indian, English etc.) * `eatType` -- restaurant type (restaurant, coffee shop, pub) * `priceRange` -- price range (low, medium, high, <£20, £20-30, >£30) * `rating` -- customer rating (low, medium, high, 1/5, 3/5, 5/5) * `familyFriendly` -- is the restaurant family-friendly (yes/no) The same MR is often repeated multiple times with different synonymous references. #### How were labels chosen? <!-- info: How were the labels chosen? --> <!-- scope: microscope --> The source MRs were generated automatically at random from a set of valid attribute values. The labels were crowdsourced and are natural language #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "input": "name[Alimentum], area[riverside], familyFriendly[yes], near[Burger King]", "target": "Alimentum is a kids friendly place in the riverside area near Burger King." } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> \| \| MRs \| Distinct MRs \| References \| \|-------------\|------\|--------------\|------------\| \| Training \|12,568\| 8,362 \| 33,525 \| \| Development \| 1,484\| 1,132 \| 4,299 \| \| Test \| 1,847\| 1,358 \| 4,693 \| \| Total \|15,899\| 10,852 \| 42,517 \| “Distinct MRs” are MRs that remain distinct even if restaurant/place names (attributes `name`, `near`) are delexicalized, i.e., replaced with a placeholder. #### 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 data are divided so that MRs in different splits do not overlap. ## 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 --> The E2E dataset is one of the largest limited-domain NLG datasets and is frequently used as a data-to-text generation benchmark. The E2E Challenge included 20 systems of very different architectures, with system outputs available for download. #### 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 dataset is much cleaner than comparable datasets, and it is also a relatively easy task, making for a straightforward evaluation. #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> 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 --> 4 special test sets for E2E were added to the GEM evaluation suite. 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 input properties was randomly reassigned. 3. For the input size, we created subpopulations based on the number of restaurant properties in the input. \| Input length \| Frequency English \| \|---------------\|-------------------\| \| 2 \| 5 \| \| 3 \| 120 \| \| 4 \| 389 \| \| 5 \| 737 \| \| 6 \| 1187 \| \| 7 \| 1406 \| \| 8 \| 774 \| \| 9 \| 73 \| \| 10 \| 2 \| #### Split Motivation <!-- info: What aspects of the model's generation capacities were the splits created to test? --> <!-- scope: periscope --> Generalization and robustness ### 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 --> Surface realization. #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `BLEU`, `METEOR`, `ROUGE` #### 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 official evaluation script combines the MT-Eval and COCO Captioning libraries with the following metrics. - BLEU - CIDEr - NIST - METEOR - ROUGE-L #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> Most previous results, including the shared task results, used the library provided by the dataset creators. The shared task also conducted a human evaluation using the following two criteria: - `Quality`: When collecting quality ratings, system outputs were presented to crowd workers together with the corresponding meaning representation, which implies that correctness of the NL utterance relative to the MR should also influence this ranking. The crowd workers were asked: “How do you judge the overall quality of the utterance in terms of its grammatical correctness, fluency, adequacy and other important factors?” - `Naturalness`: When collecting naturalness ratings, system outputs were presented to crowd workers without the corresponding meaning representation. The crowd workers were asked: “Could the utterance have been produced by a native speaker?” #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> The shared task writeup has in-depth evaluations of systems (https://www.sciencedirect.com/science/article/pii/S0885230819300919) ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The dataset was collected to showcase/test neural NLG models. It is larger and contains more lexical richness and syntactic variation than previous closed-domain NLG datasets. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> Producing a text informing/recommending a restaurant, given all and only the attributes specified on the input. #### 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 --> `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 --> Human references describing the MRs were collected by crowdsourcing on the CrowdFlower (now Appen) platform, with either textual or pictorial MRs as a baseline. The pictorial MRs were used in 20% of cases -- these yield higher lexical variation but introduce noise. #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> The dataset is focused on descriptions of restaurants. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by data curator #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> There were basic checks (length, valid characters, repetition). #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> algorithmically #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> The cleaned version of the dataset which we are using in GEM was algorithmically filtered. They used regular expressions to match all human-generated references with a more accurate input when attributes were hallucinated or dropped. Additionally, train-test overlap stemming from the transformation was removed. As a result, this data is much cleaner than the original dataset but not perfect (about 20% of instances may have misaligned slots, compared to 40% of the original data. ### 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 --> Since a crowdsourcing platform was used, the involved raters waived their rights to the data and are aware that the produced annotations can be publicly released. ### 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 --> The dataset is artificial and does not contain any description of people. ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no #### 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 source data is generated randomly, so it should not contain biases. The human references may be biased by the workers' demographic, but that was not investigated upon data collection. ## 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 --> `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 cleaned version still has data points with hallucinated or omitted attributes. #### 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 data only pertains to the restaurant domain and the included attributes. A model cannot be expected to handle other domains or attributes.
ywchoi/pubmed_abstract_0	2022-09-13T00:53:42.000Z	[ "region:us" ]	ywchoi	null	null	null	1	667	Entry not found
result-kand2-sdxl-wuerst-karlo/9e7f6f37	2023-09-19T00:24:03.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	667	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 152 num_examples: 10 download_size: 1303 dataset_size: 152 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "9e7f6f37" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EleutherAI/the_pile_deduplicated	2022-12-02T23:49:09.000Z	[ "region:us" ]	EleutherAI	null	null	null	39	666	Entry not found
lmsys/mt_bench_human_judgments	2023-07-20T18:28:15.000Z	[ "task_categories:conversational", "task_categories:question-answering", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0", "arxiv:2306.05685", "region:us" ]	lmsys	null	null	null	32	666	--- dataset_info: features: - name: question_id dtype: int64 - name: model_a dtype: string - name: model_b dtype: string - name: winner dtype: string - name: judge dtype: string - name: conversation_a list: - name: content dtype: string - name: role dtype: string - name: conversation_b list: - name: content dtype: string - name: role dtype: string - name: turn dtype: int64 splits: - name: human num_bytes: 15003469 num_examples: 3355 - name: gpt4_pair num_bytes: 10679650 num_examples: 2400 download_size: 1388888 dataset_size: 25683119 license: cc-by-4.0 task_categories: - conversational - question-answering language: - en size_categories: - 1K<n<10K --- ## Content This dataset contains 3.3K expert-level pairwise human preferences for model responses generated by 6 models in response to 80 MT-bench questions. The 6 models are GPT-4, GPT-3.5, Claud-v1, Vicuna-13B, Alpaca-13B, and LLaMA-13B. The annotators are mostly graduate students with expertise in the topic areas of each of the questions. The details of data collection can be found in our [paper](https://arxiv.org/abs/2306.05685). ## Agreement Calculation This Colab [notebook](https://colab.research.google.com/drive/1ctgygDRJhVGUJTQy8-bRZCl1WNcT8De6?usp=sharing) shows how to compute the agreement between humans and GPT-4 judge with the dataset. Our results show that humans and GPT-4 judge achieve over 80\% agreement, the same level of agreement between humans. ## Citation ``` @misc{zheng2023judging, title={Judging LLM-as-a-judge with MT-Bench and Chatbot Arena}, author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zi Lin and Zhuohan Li and Dacheng Li and Eric. P Xing and Hao Zhang and Joseph E. Gonzalez and Ion Stoica}, year={2023}, eprint={2306.05685}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
result-kand2-sdxl-wuerst-karlo/e87ec3b2	2023-09-19T00:21:50.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	666	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 153 num_examples: 10 download_size: 1306 dataset_size: 153 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "e87ec3b2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/ad45b2bb	2023-09-19T01:34:46.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	664	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1388 dataset_size: 188 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ad45b2bb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
keremberke/chest-xray-classification	2023-01-18T09:25:27.000Z	[ "task_categories:image-classification", "roboflow", "roboflow2huggingface", "Biology", "region:us" ]	keremberke	null	\	null	9	663	--- task_categories: - image-classification tags: - roboflow - roboflow2huggingface - Biology --- <div align="center"> <img width="640" alt="keremberke/chest-xray-classification" src="https://huggingface.co/datasets/keremberke/chest-xray-classification/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['NORMAL', 'PNEUMONIA'] ``` ### Number of Images ```json {'train': 4077, 'test': 582, 'valid': 1165} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/chest-xray-classification", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/2](https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/2?ref=roboflow2huggingface) ### Citation ``` ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.ai on March 31, 2022 at 3:11 PM GMT It includes 5824 images. Pneumonia are annotated in folder format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) No image augmentation techniques were applied.
Biddls/Onion_News	2023-03-25T12:57:47.000Z	[ "task_categories:summarization", "task_categories:text2text-generation", "task_categories:text-generation", "task_categories:text-classification", "language:en", "license:mit", "region:us" ]	Biddls	null	null	null	1	661	--- license: mit task_categories: - summarization - text2text-generation - text-generation - text-classification language: - en pretty_name: OnionNewsScrape --- ## This is a dataset of Onion news articles: Note - The headers and body of the news article is split by a ' #~# ' token - Lines with just the token had no body or no header and can be skipped - Feel free to use the script provided to scape the latest version, it takes about 30 mins on an i7-6850K
result-kand2-sdxl-wuerst-karlo/4b9958b5	2023-09-19T02:29:31.000Z	[ "region:us" ]	result-kand2-sdxl-wuerst-karlo	null	null	null	0	661	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 167 num_examples: 10 download_size: 1331 dataset_size: 167 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "4b9958b5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
unitxt/data	2023-10-03T13:07:44.000Z	[ "license:apache-2.0", "region:us" ]	unitxt	null	null	null	0	658	--- license: apache-2.0 ---
yuchenlin/just-eval-instruct	2023-10-07T06:44:23.000Z	[ "region:us" ]	yuchenlin	null	null	null	2	656	--- configs: - config_name: default data_files: - split: test path: "test.jsonl" - config_name: responses data_files: - split: gpt_4 path: "responses/gpt-4.json" - split: gpt_3.5_turbo path: "responses/gpt-3.5-turbo.json" - split: vicuna_7b_v1.5 path: "responses/vicuna-7b-v1.5.json" - split: Llama_2_7b_chat path: "responses/Llama-2-7b-chat-hf.json" - split: Llama_2_13b_chat path: "responses/Llama-2-13b-chat-hf.json" - split: Llama_2_70b_chat_gptq path: "responses/Llama-2-70B-chat-GPTQ.json" - config_name: judgements data_files: - split: gpt_4 path: "judgements/score_multi_gpt4/gpt-4-0314.score_multi.gpt4.jsonl" - split: gpt_3.5_turbo path: "judgements/score_multi_gpt4/gpt-3.5-turbo-0613.score_multi.gpt4.jsonl" - split: vicuna_7b_v1.5 path: "judgements/score_multi_gpt4/vicuna-7b-v1.5.score_multi.gpt4.jsonl" - split: Llama_2_7b_chat path: "judgements/score_multi_gpt4/Llama-2-7b-chat-hf.score_multi.gpt4.jsonl" - split: Llama_2_13b_chat path: "judgements/score_multi_gpt4/Llama-2-13b-chat-hf.score_multi.gpt4.jsonl" - split: Llama_2_70b_chat_gptq path: "judgements/score_multi_gpt4/Llama-2-70B-chat-GPTQ.score_multi.gpt4.jsonl" --- ## Just Eval Instruct!
openslr	2023-06-01T14:59:55.000Z	[ "task_categories:automatic-speech-recognition", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:af", "language:bn", "language:ca", "language:en", "language:es", "language:eu", "language:gl", "language:gu", "language:jv", "language:km", "language:kn", "language:ml", "language:mr", "language:my", "language:ne", "language:si", "language:st", "language:su", "language:ta", "language:te", "language:tn", "language:ve", "language:xh", "language:yo", "license:cc-by-sa-4.0", "region:us" ]	null	OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. We intend to be a convenient place for anyone to put resources that they have created, so that they can be downloaded publicly.	SLR32: @inproceedings{van-niekerk-etal-2017, title = {{Rapid development of TTS corpora for four South African languages}}, author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson and Martin Jansche and Linne Ha}, booktitle = {Proc. Interspeech 2017}, pages = {2178--2182}, address = {Stockholm, Sweden}, month = aug, year = {2017}, URL = {http://dx.doi.org/10.21437/Interspeech.2017-1139} } SLR35, SLR36, SLR52, SLR53, SLR54: @inproceedings{kjartansson-etal-sltu2018, title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}}, author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha}, booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, year = {2018}, address = {Gurugram, India}, month = aug, pages = {52--55}, URL = {https://dx.doi.org/10.21437/SLTU.2018-11}, } SLR41, SLR42, SLR43, SLR44: @inproceedings{kjartansson-etal-tts-sltu2018, title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}}, author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin}, booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, year = {2018}, address = {Gurugram, India}, month = aug, pages = {66--70}, URL = {https://dx.doi.org/10.21437/SLTU.2018-14} } SLR63, SLR64, SLR65, SLR66, SLR78, SLR79: @inproceedings{he-etal-2020-open, title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}}, author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, month = may, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, pages = {6494--6503}, url = {https://www.aclweb.org/anthology/2020.lrec-1.800}, ISBN = "{979-10-95546-34-4}, } SLR69, SLR76, SLR77: @inproceedings{kjartansson-etal-2020-open, title = {{Open-Source High Quality Speech Datasets for Basque, Catalan and Galician}}, author = {Kjartansson, Oddur and Gutkin, Alexander and Butryna, Alena and Demirsahin, Isin and Rivera, Clara}, 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)}, year = {2020}, pages = {21--27}, month = may, address = {Marseille, France}, publisher = {European Language Resources association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.sltu-1.3}, ISBN = {979-10-95546-35-1}, } SLR71, SLR71, SLR72, SLR73, SLR74, SLR75: @inproceedings{guevara-rukoz-etal-2020-crowdsourcing, title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}}, author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, year = {2020}, month = may, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.801}, pages = {6504--6513}, ISBN = {979-10-95546-34-4}, } SLR80 @inproceedings{oo-etal-2020-burmese, title = {{Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech}}, author = {Oo, Yin May and Wattanavekin, Theeraphol and Li, Chenfang and De Silva, Pasindu and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Kjartansson, Oddur and Gutkin, Alexander}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, month = may, year = {2020}, pages = "6328--6339", address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.777}, ISBN = {979-10-95546-34-4}, } SLR86 @inproceedings{gutkin-et-al-yoruba2020, title = {{Developing an Open-Source Corpus of Yoruba Speech}}, author = {Alexander Gutkin and Işın Demirşahin and Oddur Kjartansson and Clara Rivera and Kọ́lá Túbọ̀sún}, booktitle = {Proceedings of Interspeech 2020}, pages = {404--408}, month = {October}, year = {2020}, address = {Shanghai, China}, publisher = {International Speech and Communication Association (ISCA)}, doi = {10.21437/Interspeech.2020-1096}, url = {https://dx.doi.org/10.21437/Interspeech.2020-1096}, }	null	11	655	--- pretty_name: OpenSLR annotations_creators: - found language_creators: - found language: - af - bn - ca - en - es - eu - gl - gu - jv - km - kn - ml - mr - my - ne - si - st - su - ta - te - tn - ve - xh - yo language_bcp47: - en-GB - en-IE - en-NG - es-CL - es-CO - es-PE - es-PR license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: null dataset_info: - config_name: SLR41 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2423902 num_examples: 5822 download_size: 1890792360 dataset_size: 2423902 - config_name: SLR42 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1427984 num_examples: 2906 download_size: 866086951 dataset_size: 1427984 - config_name: SLR43 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1074005 num_examples: 2064 download_size: 800375645 dataset_size: 1074005 - config_name: SLR44 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1776827 num_examples: 4213 download_size: 1472252752 dataset_size: 1776827 - config_name: SLR63 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2016587 num_examples: 4126 download_size: 1345876299 dataset_size: 2016587 - config_name: SLR64 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 810375 num_examples: 1569 download_size: 712155683 dataset_size: 810375 - config_name: SLR65 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2136447 num_examples: 4284 download_size: 1373304655 dataset_size: 2136447 - config_name: SLR66 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1898335 num_examples: 4448 download_size: 1035127870 dataset_size: 1898335 - config_name: SLR69 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1647263 num_examples: 4240 download_size: 1848659543 dataset_size: 1647263 - config_name: SLR35 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 73565374 num_examples: 185076 download_size: 18900105726 dataset_size: 73565374 - config_name: SLR36 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 88942337 num_examples: 219156 download_size: 22996553929 dataset_size: 88942337 - config_name: SLR70 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1339608 num_examples: 3359 download_size: 1213955196 dataset_size: 1339608 - config_name: SLR71 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1676273 num_examples: 4374 download_size: 1445365903 dataset_size: 1676273 - config_name: SLR72 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1876301 num_examples: 4903 download_size: 1612030532 dataset_size: 1876301 - config_name: SLR73 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2084052 num_examples: 5447 download_size: 1940306814 dataset_size: 2084052 - config_name: SLR74 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 237395 num_examples: 617 download_size: 214181314 dataset_size: 237395 - config_name: SLR75 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1286937 num_examples: 3357 download_size: 1043317004 dataset_size: 1286937 - config_name: SLR76 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2756507 num_examples: 7136 download_size: 3041125513 dataset_size: 2756507 - config_name: SLR77 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2217652 num_examples: 5587 download_size: 2207991775 dataset_size: 2217652 - config_name: SLR78 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2121986 num_examples: 4272 download_size: 1743222102 dataset_size: 2121986 - config_name: SLR79 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2176539 num_examples: 4400 download_size: 1820919115 dataset_size: 2176539 - config_name: SLR80 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1308651 num_examples: 2530 download_size: 948181015 dataset_size: 1308651 - config_name: SLR86 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1378801 num_examples: 3583 download_size: 907065562 dataset_size: 1378801 - config_name: SLR32 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 4544052380 num_examples: 9821 download_size: 3312884763 dataset_size: 4544052380 - config_name: SLR52 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 77369899 num_examples: 185293 download_size: 14676484074 dataset_size: 77369899 - config_name: SLR53 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 88073248 num_examples: 218703 download_size: 14630810921 dataset_size: 88073248 - config_name: SLR54 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 62735822 num_examples: 157905 download_size: 9328247362 dataset_size: 62735822 - config_name: SLR83 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 7098985 num_examples: 17877 download_size: 7229890819 dataset_size: 7098985 config_names: - SLR32 - SLR35 - SLR36 - SLR41 - SLR42 - SLR43 - SLR44 - SLR52 - SLR53 - SLR54 - SLR63 - SLR64 - SLR65 - SLR66 - SLR69 - SLR70 - SLR71 - SLR72 - SLR73 - SLR74 - SLR75 - SLR76 - SLR77 - SLR78 - SLR79 - SLR80 - SLR83 - SLR86 --- # Dataset Card for openslr ## 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.openslr.org/ - Repository: [Needs More Information] - Paper: [Needs More Information] - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. Currently, following resources are available: #### SLR32: High quality TTS data for four South African languages (af, st, tn, xh). This data set contains multi-speaker high quality transcribed audio data for four languages of South Africa. The data set consists of wave files, and a TSV file transcribing the audio. In each folder, the file line_index.tsv contains a FileID, which in turn contains the UserID and the Transcription of audio in the file. The data set has had some quality checks, but there might still be errors. This data set was collected by as a collaboration between North West University and Google. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See https://github.com/google/language-resources#license for license information. Copyright 2017 Google, Inc. #### SLR35: Large Javanese ASR training data set. This data set contains transcribed audio data for Javanese (~185K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/35/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017 Google, Inc. #### SLR36: Large Sundanese ASR training data set. This data set contains transcribed audio data for Sundanese (~220K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/36/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017 Google, Inc. #### SLR41: High quality TTS data for Javanese. This data set contains high-quality transcribed audio data for Javanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Gadjah Mada University in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/41/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR42: High quality TTS data for Khmer. This data set contains high-quality transcribed audio data for Khmer. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/42/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR43: High quality TTS data for Nepali. This data set contains high-quality transcribed audio data for Nepali. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in Nepal. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/43/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR44: High quality TTS data for Sundanese. This data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Universitas Pendidikan Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/44/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR52: Large Sinhala ASR training data set. This data set contains transcribed audio data for Sinhala (~185K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/52/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR53: Large Bengali ASR training data set. This data set contains transcribed audio data for Bengali (~196K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/53/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR54: Large Nepali ASR training data set. This data set contains transcribed audio data for Nepali (~157K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/54/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR63: Crowdsourced high-quality Malayalam multi-speaker speech data set This data set contains transcribed high-quality audio of Malayalam sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/63/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR64: Crowdsourced high-quality Marathi multi-speaker speech data set This data set contains transcribed high-quality audio of Marathi sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/64/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR65: Crowdsourced high-quality Tamil multi-speaker speech data set This data set contains transcribed high-quality audio of Tamil sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/65/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR66: Crowdsourced high-quality Telugu multi-speaker speech data set This data set contains transcribed high-quality audio of Telugu sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/66/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR69: Crowdsourced high-quality Catalan multi-speaker speech data set This data set contains transcribed high-quality audio of Catalan sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/69/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR70: Crowdsourced high-quality Nigerian English speech data set This data set contains transcribed high-quality audio of Nigerian English sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/70/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR71: Crowdsourced high-quality Chilean Spanish speech data set This data set contains transcribed high-quality audio of Chilean Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/71/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR72: Crowdsourced high-quality Colombian Spanish speech data set This data set contains transcribed high-quality audio of Colombian Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/72/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR73: Crowdsourced high-quality Peruvian Spanish speech data set This data set contains transcribed high-quality audio of Peruvian Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/73/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR74: Crowdsourced high-quality Puerto Rico Spanish speech data set This data set contains transcribed high-quality audio of Puerto Rico Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/74/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR75: Crowdsourced high-quality Venezuelan Spanish speech data set This data set contains transcribed high-quality audio of Venezuelan Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/75/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR76: Crowdsourced high-quality Basque speech data set This data set contains transcribed high-quality audio of Basque sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/76/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR77: Crowdsourced high-quality Galician speech data set This data set contains transcribed high-quality audio of Galician sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/77/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR78: Crowdsourced high-quality Gujarati multi-speaker speech data set This data set contains transcribed high-quality audio of Gujarati sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/78/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR79: Crowdsourced high-quality Kannada multi-speaker speech data set This data set contains transcribed high-quality audio of Kannada sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/79/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR80: Crowdsourced high-quality Burmese speech data set This data set contains transcribed high-quality audio of Burmese sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/80/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR83: Crowdsourced high-quality UK and Ireland English Dialect speech data set This data set contains transcribed high-quality audio of English sentences recorded by volunteers speaking different dialects of the language. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.csv contains a line id, an anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The recordings from the Welsh English speakers were collected in collaboration with Cardiff University. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/83/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR86: Crowdsourced high-quality multi-speaker speech data set This data set contains transcribed high-quality audio of sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/86/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019, 2020 Google, Inc. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Javanese, Khmer, Nepali, Sundanese, Malayalam, Marathi, Tamil, Telugu, Catalan, Nigerian English, Chilean Spanish, Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati, Kannada, Afrikaans, Sesotho, Setswana and isiXhosa. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, called path and its sentence. #### SLR32, SLR35, SLR36, SLR41, SLR42, SLR43, SLR44, SLR52, SLR53, SLR54, SLR63, SLR64, SLR65, SLR66, SLR69, SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80, SLR86 ``` { 'path': '/home/cahya/.cache/huggingface/datasets/downloads/extracted/4d9cf915efc21110199074da4d492566dee6097068b07a680f670fcec9176e62/su_id_female/wavs/suf_00297_00037352660.wav' 'audio': {'path': '/home/cahya/.cache/huggingface/datasets/downloads/extracted/4d9cf915efc21110199074da4d492566dee6097068b07a680f670fcec9176e62/su_id_female/wavs/suf_00297_00037352660.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'sentence': 'Panonton ting haruleng ningali Kelly Clarkson keur nyanyi di tipi', } ``` ### Data Fields - `path`: The path to the audio file. - `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 There is only one "train" split for all configurations and the number of examples are: \| \| Number of examples \| \|:------\|---------------------:\| \| SLR41 \| 5822 \| \| SLR42 \| 2906 \| \| SLR43 \| 2064 \| \| SLR44 \| 4213 \| \| SLR63 \| 4126 \| \| SLR64 \| 1569 \| \| SLR65 \| 4284 \| \| SLR66 \| 4448 \| \| SLR69 \| 4240 \| \| SLR35 \| 185076 \| \| SLR36 \| 219156 \| \| SLR70 \| 3359 \| \| SLR71 \| 4374 \| \| SLR72 \| 4903 \| \| SLR73 \| 5447 \| \| SLR74 \| 617 \| \| SLR75 \| 3357 \| \| SLR76 \| 7136 \| \| SLR77 \| 5587 \| \| SLR78 \| 4272 \| \| SLR79 \| 4400 \| \| SLR80 \| 2530 \| \| SLR86 \| 3583 \| \| SLR32 \| 9821 \| \| SLR52 \| 185293 \| \| SLR53 \| 218703 \| \| SLR54 \| 157905 \| \| SLR83 \| 17877 \| ## 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 The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Each dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License ([CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)). See https://github.com/google/language-resources#license or the resource page on [OpenSLR](https://openslr.org/resources.php) for more information. ### Citation Information #### SLR32 ``` @inproceedings{van-niekerk-etal-2017, title = {{Rapid development of TTS corpora for four South African languages}}, author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson and Martin Jansche and Linne Ha}, booktitle = {Proc. Interspeech 2017}, pages = {2178--2182}, address = {Stockholm, Sweden}, month = aug, year = {2017}, URL = {https://dx.doi.org/10.21437/Interspeech.2017-1139} } ``` #### SLR35, SLR36, SLR52, SLR53, SLR54 ``` @inproceedings{kjartansson-etal-sltu2018, title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}}, author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha}, booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, year = {2018}, address = {Gurugram, India}, month = aug, pages = {52--55}, URL = {https://dx.doi.org/10.21437/SLTU.2018-11}, } ``` #### SLR41, SLR42, SLR43, SLR44 ``` @inproceedings{kjartansson-etal-tts-sltu2018, title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}}, author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin}, booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, year = {2018}, address = {Gurugram, India}, month = aug, pages = {66--70}, URL = {https://dx.doi.org/10.21437/SLTU.2018-14} } ``` #### SLR63, SLR64, SLR65, SLR66, SLR78, SLR79 ``` @inproceedings{he-etal-2020-open, title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}}, author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, month = may, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, pages = {6494--6503}, url = {https://www.aclweb.org/anthology/2020.lrec-1.800}, ISBN = "{979-10-95546-34-4}, } ``` #### SLR69, SLR76, SLR77 ``` @inproceedings{kjartansson-etal-2020-open, title = {{Open-Source High Quality Speech Datasets for Basque, Catalan and Galician}}, author = {Kjartansson, Oddur and Gutkin, Alexander and Butryna, Alena and Demirsahin, Isin and Rivera, Clara}, 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)}, year = {2020}, pages = {21--27}, month = may, address = {Marseille, France}, publisher = {European Language Resources association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.sltu-1.3}, ISBN = {979-10-95546-35-1}, } ``` #### SLR70, SLR71, SLR72, SLR73, SLR74, SLR75 ``` @inproceedings{guevara-rukoz-etal-2020-crowdsourcing, title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}}, author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, year = {2020}, month = may, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.801}, pages = {6504--6513}, ISBN = {979-10-95546-34-4}, } ``` #### SLR80 ``` @inproceedings{oo-etal-2020-burmese, title = {{Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech}}, author = {Oo, Yin May and Wattanavekin, Theeraphol and Li, Chenfang and De Silva, Pasindu and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Kjartansson, Oddur and Gutkin, Alexander}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, month = may, year = {2020}, pages = "6328--6339", address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.777}, ISBN = {979-10-95546-34-4}, } ``` #### SLR86 ``` @inproceedings{gutkin-et-al-yoruba2020, title = {{Developing an Open-Source Corpus of Yoruba Speech}}, author = {Alexander Gutkin and I{\c{s}}{\i}n Demir{\c{s}}ahin and Oddur Kjartansson and Clara Rivera and K\d{\'o}lá Túb\d{\`o}sún}, booktitle = {Proceedings of Interspeech 2020}, pages = {404--408}, month = {October}, year = {2020}, address = {Shanghai, China}, publisher = {International Speech and Communication Association (ISCA)}, doi = {10.21437/Interspeech.2020-1096}, url = {https://dx.doi.org/10.21437/Interspeech.2020-1096}, } ``` ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
conceptofmind/t0_submix_original	2023-05-24T18:32:56.000Z	[ "region:us" ]	conceptofmind	null	null	null	19	655	--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: task_source dtype: string - name: task_name dtype: string - name: template_type dtype: string splits: - name: train num_bytes: 4602180562 num_examples: 1650308 download_size: 2738296803 dataset_size: 4602180562 --- # Dataset Card for "t0_submix_original" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BeIR/scifact	2022-10-23T06:01:22.000Z	[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]	BeIR	null	null	null	1	654	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## 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 [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
chromadb/state_of_the_union	2023-07-07T18:13:04.000Z	[ "region:us" ]	chromadb	null	null	null	0	654	--- dataset_info: features: - name: id dtype: string - name: embedding sequence: float64 - name: metadata struct: - name: source dtype: string - name: document dtype: string splits: - name: data num_bytes: 556545 num_examples: 42 download_size: 519613 dataset_size: 556545 --- # Dataset Card for "state_of_the_union" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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