davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
kevmo314/YouTube-Speech-Dataset	--- license: other ---
open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M	--- pretty_name: Evaluation run of cerebras/Cerebras-GPT-111M dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cerebras/Cerebras-GPT-111M](https://huggingface.co/cerebras/Cerebras-GPT-111M)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T19:15:45.776483](https://huggingface.co/datasets/open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M/blob/main/results_2023-09-22T19-15-45.776483.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\": 0.0010486577181208054,\n\ \ \"em_stderr\": 0.00033145814652193176,\n \"f1\": 0.021427223154362497,\n\ \ \"f1_stderr\": 0.0008720566428263053,\n \"acc\": 0.23875295974743488,\n\ \ \"acc_stderr\": 0.00701912891202994\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652193176,\n\ \ \"f1\": 0.021427223154362497,\n \"f1_stderr\": 0.0008720566428263053\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.47750591949486976,\n\ \ \"acc_stderr\": 0.01403825782405988\n }\n}\n```" repo_url: https://huggingface.co/cerebras/Cerebras-GPT-111M 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_19T13_47_12.878137 path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:47:12.878137.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_22T19_15_45.776483 path: - '/details_harness\|drop\|3_2023-09-22T19-15-45.776483.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-22T19-15-45.776483.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T19_15_45.776483 path: - '/details_harness\|gsm8k\|5_2023-09-22T19-15-45.776483.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-22T19-15-45.776483.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hellaswag\|10_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:47:12.878137.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_47_12.878137 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:47:12.878137.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T19_15_45.776483 path: - '/details_harness\|winogrande\|5_2023-09-22T19-15-45.776483.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-22T19-15-45.776483.parquet' - config_name: results data_files: - split: 2023_07_19T13_47_12.878137 path: - results_2023-07-19T13:47:12.878137.parquet - split: 2023_09_22T19_15_45.776483 path: - results_2023-09-22T19-15-45.776483.parquet - split: latest path: - results_2023-09-22T19-15-45.776483.parquet --- # Dataset Card for Evaluation run of cerebras/Cerebras-GPT-111M ## Dataset Description - Homepage: - Repository: https://huggingface.co/cerebras/Cerebras-GPT-111M - 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 [cerebras/Cerebras-GPT-111M](https://huggingface.co/cerebras/Cerebras-GPT-111M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T19:15:45.776483](https://huggingface.co/datasets/open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M/blob/main/results_2023-09-22T19-15-45.776483.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": 0.0010486577181208054, "em_stderr": 0.00033145814652193176, "f1": 0.021427223154362497, "f1_stderr": 0.0008720566428263053, "acc": 0.23875295974743488, "acc_stderr": 0.00701912891202994 }, "harness\|drop\|3": { "em": 0.0010486577181208054, "em_stderr": 0.00033145814652193176, "f1": 0.021427223154362497, "f1_stderr": 0.0008720566428263053 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.47750591949486976, "acc_stderr": 0.01403825782405988 } } ``` ### 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]
Nexdata/Human_Pose_Recognition_Data	--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Human_Pose_Recognition_Data ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.nexdata.ai/datasets/1132?source=Huggingface - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary 10,000 People - Human Pose Recognition Data. This dataset includes indoor and outdoor scenes.This dataset covers males and females. Age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The data diversity includes different shooting heights, different ages, different light conditions, different collecting environment, clothes in different seasons, multiple human poses. For each subject, the labels of gender, race, age, collecting environment and clothes were annotated. The data can be used for human pose recognition and other tasks. For more details, please refer to the link: https://www.nexdata.ai/datasets/1132?source=Huggingface ### Supported Tasks and Leaderboards object-detection, computer-vision: The dataset can be used to train a model for object detection. ### 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 Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
Chaymaa/grdf-inference-aug-iter2	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 38284884.0 num_examples: 456 - name: valid num_bytes: 10478667.684210526 num_examples: 113 - name: test num_bytes: 118581.31578947368 num_examples: 1 download_size: 46290889 dataset_size: 48882133.0 --- # Dataset Card for "grdf-inference-aug-iter2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Graphcore/wikipedia-bert-128	--- language: - en license: - cc-by-sa-3.0 ---
brainer/korean-medicine-prescription	--- dataset_info: features: - name: STND_Y dtype: int64 - name: IDV_ID dtype: int64 - name: KEY_SEQ dtype: int64 - name: SEQ_NO dtype: int64 - name: SEX dtype: int64 - name: AGE_GROUP dtype: int64 - name: SIDO dtype: int64 - name: RECU_FR_DT dtype: string - name: GNL_NM_CD dtype: string - name: DD_MQTY_FREQ dtype: float64 - name: DD_EXEC_FREQ dtype: int64 - name: MDCN_EXEC_FREQ dtype: int64 - name: UN_COST dtype: float64 - name: AMT dtype: int64 - name: DATA_STD_DT dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4651797950 num_examples: 32053871 download_size: 903450347 dataset_size: 4651797950 configs: - config_name: default data_files: - split: train path: data/train-* --- ```json { 'STND_Y': '기준년도', 'IDV_ID': '가입자 일련번호', 'KEY_SEQ': '진료내역 일련번호진료내역 일련번호', 'SEQ_NO': '일련번호', 'SEX': '성별코드', 'AGE_GROUP': '연령대 코드', 'SIDO': '시도코드', 'RECU_FR_DT': '요양개시일자', 'GNL_NM_CD': '약품 일반성분명 코드', 'DD_MQTY_FREQ': '1회투약량', 'DD_EXEC_FREQ': '1일투약량', 'MDCN_EXEC_FREQ': '총투여일수또는실시횟수', 'UN_COST': '단가', 'AMT': '금액', 'DATA_STD_DT': '데이터 기준일자' } ``` [데이터 출처](https://www.data.go.kr/data/15007117/fileData.do) [출처1](http://www.seoulhealth.kr/down/pdfView?atchId=595&pdfId=595&isMobile=N), [출처2](https://www.hira.or.kr/cms/participation/05/07/__icsFiles/afieldfile/2014/05/16/4_1.pdf), [출처3](http://www.khmsri.or.kr/common/file_download.jsp?filePath=/ewmri/upload/board/ewmri_seminar/upfile/&fileName=%EC%B2%AD%EA%B5%AC%EB%8D%B0%EC%9D%B4%ED%84%B0%EC%86%8C%EA%B0%9C_%EA%B9%80%EC%A7%80%EC%95%A0_%EA%B2%BD%ED%9D%AC%EB%8C%80.pdf) \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 기준년도 \| STND_Y \| \| 해당 정보의 기준년도를 제공함 \| \| 2 \| 가입자 일련번호 \| IDV_ID \| \| 해당가입자에 부여한 일련번호 (1 ~ 1,000,000) \| \| 3 \| 진료내역 일련번호 \| KEY_SEQ \| \| 해당진료내역에 대한 일련번호 \| \| 4 \| 일련번호 \| SEQ_NO \| \| 해당 약품 일련번호 \| \| 5 \| 성별코드 \| SEX \| \| 해당 정보 대상자의 성별을 제공함 (성별: 1(남자), 2(여자)) \| \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| 그룹 \| 연령대 \| 그룹 \| 연령대 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 5 \| 연령대 코드 \| AGE_GROUP \| \| 기준년도에 수진자의 나이를 5세 단위로 그룹화하여 구분한 코드 (총 18개 그룹) \| 1 \| 0~4세 \| 10 \| 45~49세 \| \| \| \| \| \| 0~84세까지 5세 단위 그룹화, 85세 이상은 85+로 그룹화 \| 2 \| 5~9세 \| 11 \| 50~54세 \| \| \| \| \| \| \| 3 \| 10~14세 \| 12 \| 55~59세 \| \| \| \| \| \| \| 4 \| 15~19세 \| 13 \| 60~64세 \| \| \| \| \| \| \| 5 \| 20~24세 \| 14 \| 65~69세 \| \| \| \| \| \| \| 6 \| 25~29세 \| 15 \| 70~74세 \| \| \| \| \| \| \| 7 \| 30~34세 \| 16 \| 75~79세 \| \| \| \| \| \| \| 8 \| 35~39세 \| 17 \| 80~84세 \| \| \| \| \| \| \| 9 \| 40~44세 \| 18 \| 85세+ \| \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| 코드명 \| 시도명 \| 코드명 \| 시도명 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 6 \| 시도코드 \| SIDO \| \| 해당 수진자 거주지의 시도코드 (2012년부터 세종특별자치시가 신규로 편입됨에 따라, 2011년까지의 데이터에는 해당 항목이 존재하지 않음) \| 11 \| 서울특별시 \| 42 \| 강원도 \| \| \| \| \| \| \| 26 \| 부산광역시 \| 43 \| 충청북도 \| \| \| \| \| \| \| 27 \| 대구광역시 \| 44 \| 충청남도 \| \| \| \| \| \| \| 28 \| 인천광역시 \| 45 \| 전라북도 \| \| \| \| \| \| \| 29 \| 광주광역시 \| 46 \| 전라남도 \| \| \| \| \| \| \| 30 \| 대전광역시 \| 47 \| 경상북도 \| \| \| \| \| \| \| 31 \| 울산광역시 \| 48 \| 경상남도 \| \| \| \| \| \| \| 36 \| 세종특별자치시 \| 49 \| 제주특별자치도 \| \| \| \| \| \| \| 41 \| 경기도 \| \| \| \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| 구분 \| 의과_보건기관 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 8 \| 서식코드 \| FORM_CD \| \| 명세서 서식구분을 위한 코드; 의과_보건기관에서 진료한 환자의 진료형태를 구분함 \| 02 \| 의과 입원 \| \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| 구분 \| 의과_보건기관 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 9 \| 진료과목 코드 \| DSBJT_CD \| \| 의과 26종의 진료과목코드에 따라 병원급 이상의 진료기관일 경우 실제진료를 받은 진료과목, 의원급 의료기관일 경우 상병 명에 해당되는 진료과목 \| 0 \| 일반의 \| \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| \| --- \| --- \| --- \| --- \| --- \| \| 10 \| 주상병 코드 \| MAIN_SICK \| \| 명세서 상의 주상병의 분류기호 - 통계청 고시에 따른「한국표준 질병·사인 분류 4, 5, 6차 상병 분류 기호 참조 \| \| 11 \| 부상병 코드 \| SUB_SICK \| \| 명세서 상의 주된 상병분류기호 외의 추가 상병(부상병)의 분류 기호 - 결측(ZZ), 정상 또는 해당사항 없음(-)으로 표시 - 통계청 고시에 따른「한국표준 질병·사인 분류 4, 5, 6차 상병 분류 기호 참조 \| \| 12 \| 요양일수 \| VSCN \| \| 수진자가 요양급여를 받은 실 일수 - 입원 또는 내원일수에 원내 투약일수를 산입하여 기재 - 내원일수는 초진과 재진을 포함함 \| \| 13 \| 입내원 일수 \| RECN \| \| (입원진료) 수진자가 진료를 받기 위해 요양기관에 입원한 날부터 퇴원 일까지의 실 일수 - (내원진료) 수진자가 내원하여 진료를 받은 실 일 수 \| \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| 요양기관 종류 \| 건강보험 \| 의료급여 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 14 \| 심결가산율 \| EDEC_ADD_RT \| \| 요양개시일자 기준으로 종별 규모에 따라 시설, 인력, 장비 등의 투자비용 등을 고려하여 요양기관 종별에 따라 가산 적용되는 진료비의 가산율(%) \| 상급 종합병원 \| 30% \| 22% \| \| \| \| \| \| \| 종합병원 \| 25% \| 18% \| \| \| \| \| \| \| 병원(요양병원 포함) \| 20% \| 15% \| \| \| \| \| \| \| 의원, 보건의료원 등 \| 15% \| 11% \| \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| \| --- \| --- \| --- \| --- \| --- \| \| 15 \| 심결요양 급여비용 총액 \| EDEC_TRAMT \| \| 심결요양급여비용총액은 정산심사결과 수진자 본인이 부담해야 될 금액인「심결본인부담금」과 보험자가 부담해야 할 「심결 보험자부담금」합친 금액 \| \| 16 \| 심결본인 부담금 \| EDEC_SBRDN_AMT \| \| 요양급여비용심사결과를 통해 결정된 수진자 본인이 부담해야 할 부담금 - 국민건강보험법 시행령 [별표2]에 따른 본인일부부담금(동법 시행령 별표2 제4호 및 제5호에 따른 금액을 제외)에서 10원 미만 절사한 금액 \| \| 17 \| 심결 보험자 부담금 \| EDEC_JBRDN_AMT \| \| 심결요양급여비용총액에서 본인일부부담금을 제외한 금액으로 보험자가 부담하여야 하는 금액 \| \| 18 \| 총처방 일수 \| TOT_PRES_DD_CNT \| \| 처방전을 발급한 경우에 해당 처방전에 따라 조제 투약하도록 처방한 일수의 합 \| \| 19 \| 데이터 기준일자 \| DATA_STD_DT \| \| 데이터 작성 기준 일자 \| \| 연번 \| 제공항목 \| 표준항목명 \| 영문명 \| 설명 \| \| --- \| --- \| --- \| --- \| --- \| \| \| 단가 \| UN_COST \| \| 처방 내역상의 의약품 단가 제품코드 약품명 제약사명 규격단위 상한금액 실구입가 단가 641100180 A연질 캅셀 A제약 1캅셀 245 247 245 \| \| \| 금액 \| AMT \| \| 단가, 1회 투약량, 1일 투약량, 총투여일수를 곱한 금액 예시) 652101370 트렌탈 400 서방정 1회 1정, 1일 2회, 2일 투여시 ⥤ 190(원)×1(정)×2(회)×2(일)= 760원 ※ 단, 규격에 따라서 금액이 상이하게 나올수 있으니 매 월 고시되는 보건복지부 고시「약제급여목록 및 급여 상한금액표」참고하여야 함 \|
iambestfeed/corpus_gen	--- dataset_info: features: - name: text dtype: string - name: level dtype: string - name: topic dtype: string splits: - name: train num_bytes: 301290168 num_examples: 109990 download_size: 121315554 dataset_size: 301290168 configs: - config_name: default data_files: - split: train path: data/train-* ---
yashnbx/l27b-E02-large-b05-0584-3	--- dataset_info: features: - name: id dtype: int64 - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: test num_bytes: 1011775 num_examples: 146 - name: train num_bytes: 4032267 num_examples: 584 download_size: 831330 dataset_size: 5044042 --- # Dataset Card for "l27b-E02-large-b05-0584-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/tartaglia_genshin	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tartaglia_genshin This is the dataset of tartaglia_genshin, containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 200 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 419 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 200 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 200 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 200 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 200 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 200 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 419 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 419 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 419 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
bigcode/commits-pjj-2048	--- dataset_info: features: - name: commit dtype: string - name: subject dtype: string - name: old_file dtype: string - name: new_file dtype: string - name: new_contents dtype: string - name: old_contents dtype: string - name: lang dtype: string - name: proba dtype: float64 splits: - name: train num_bytes: 49066548725.366234 num_examples: 1847311 download_size: 3264623361 dataset_size: 49066548725.366234 --- # Dataset Card for "commits-pjj-2048" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-131000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1094022 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
brantfetter/primary	--- license: cc-by-nd-4.0 ---
arthurmluz/xlsum_data-wiki_cstnews_1024_results	--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 29742589 num_examples: 7175 download_size: 18278365 dataset_size: 29742589 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "xlsum_data-wiki_cstnews_1024_results" rouge={'rouge1': 0.21341914887481395, 'rouge2': 0.055688000356489714, 'rougeL': 0.13199232049785112, 'rougeLsum': 0.13199232049785112} Bert={'precision': 0.6575144631688188, 'recall': 0.7245010691569658, 'f1': 0.6890005766888528} mover = 0.568884719739508
waveww/guanaco-llama2-1k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966693 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HeshamMamdouh/dataset-LANS-processesed	--- dataset_info: features: - name: address dtype: string - name: title dtype: string - name: paragraph sequence: string - name: summary dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 2708388021 num_examples: 282281 download_size: 356101098 dataset_size: 2708388021 --- # Dataset Card for "dataset-LANS-processesed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pphuc25/gsm8k_500_new	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 270557.99848369975 num_examples: 500 download_size: 157994 dataset_size: 270557.99848369975 configs: - config_name: default data_files: - split: test path: data/test-* ---
ChunB1/books_adv	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 109344916 num_examples: 960000 download_size: 61970737 dataset_size: 109344916 configs: - config_name: default data_files: - split: train path: data/train-* ---
davanstrien/embedded-cards	--- dataset_info: features: - name: id dtype: string - name: author dtype: string - name: last_modified dtype: timestamp[us, tz=UTC] - name: downloads dtype: int64 - name: likes dtype: int64 - name: paperswithcode_id dtype: string - name: tags sequence: string - name: lastModified dtype: timestamp[us, tz=UTC] - name: createdAt dtype: string - name: key dtype: string - name: created dtype: timestamp[us] - name: card dtype: string - name: embedding sequence: float32 - name: library_name dtype: string - name: pipeline_tag dtype: string - name: mask_token dtype: 'null' - name: card_data dtype: 'null' - name: widget_data dtype: 'null' - name: model_index dtype: 'null' - name: config dtype: 'null' - name: transformers_info dtype: 'null' - name: spaces dtype: 'null' - name: safetensors dtype: 'null' - name: transformersInfo dtype: 'null' - name: modelId dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 472972552 num_examples: 85143 download_size: 309488689 dataset_size: 472972552 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/neon_nikke	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of neon/ネオン/尼恩/네온 (Nikke: Goddess of Victory) This is the dataset of neon/ネオン/尼恩/네온 (Nikke: Goddess of Victory), containing 141 images and their tags. The core tags of this character are `glasses, green_eyes, hat, breasts, long_hair, white_headwear, beret, bangs, white_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 141 \| 191.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 141 \| 109.56 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 304 \| 217.70 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 141 \| 167.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 304 \| 302.76 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/neon_nikke', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 19 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1boy, 1girl, hetero, solo_focus, navel, nipples, penis, pussy, thighhighs, blush, uncensored, vaginal, yellow_eyes, open_mouth, medium_breasts, sex_from_behind, nude, cross-section, from_below, sailor_collar, testicles \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blue_neckerchief, bottomless, looking_at_viewer, medium_breasts, navel, pussy, solo, spread_legs, anus, ass, leg_up, no_bra, sailor_collar, serafuku, underboob, white_thighhighs, yellow_eyes, open_mouth, small_breasts, uncensored, from_below, nipple_slip, rock, shirt \| \| 2 \| 18 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, solo, looking_at_viewer, pleated_skirt, white_thighhighs, short_sleeves, white_skirt, sailor_collar, open_mouth, ass, hair_ornament, one-piece_swimsuit, rifle, brown_gloves, grey_hair, looking_back, smile, swimsuit_under_clothes, thigh_boots, white_shirt, high_heels, holding, school_uniform, white_footwear \| \| 3 \| 11 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, ass, looking_at_viewer, skindentation, solo, thighs, white_shirt, open_mouth, white_thighhighs, outdoors, short_sleeves, bag, sailor_hat, crop_top, from_behind, looking_back, pleated_skirt, serafuku, medium_breasts, red-framed_eyewear, short_hair, belt_pouch, high_heels, neckerchief, panties, under-rim_eyewear, white_footwear, :d, building, midriff, miniskirt, sailor_shirt, shoes, white_skirt \| \| 4 \| 19 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| looking_at_viewer, open_mouth, 1girl, large_breasts, sailor_collar, grey_hair, hairband, navel, white_bikini, cleavage, outdoors, pink_skirt, red-framed_eyewear, solo, blush, wet, choker, day, hair_bow, innertube, bikini_skirt, front-tie_top, holding, side-tie_bikini_bottom, sky, cloud, medium_breasts, :d, beach, pink_bikini, water \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1boy \| 1girl \| hetero \| solo_focus \| navel \| nipples \| penis \| pussy \| thighhighs \| blush \| uncensored \| vaginal \| yellow_eyes \| open_mouth \| medium_breasts \| sex_from_behind \| nude \| cross-section \| from_below \| sailor_collar \| testicles \| blue_neckerchief \| bottomless \| looking_at_viewer \| solo \| spread_legs \| anus \| ass \| leg_up \| no_bra \| serafuku \| underboob \| white_thighhighs \| small_breasts \| nipple_slip \| rock \| shirt \| pleated_skirt \| short_sleeves \| white_skirt \| hair_ornament \| one-piece_swimsuit \| rifle \| brown_gloves \| grey_hair \| looking_back \| smile \| swimsuit_under_clothes \| thigh_boots \| white_shirt \| high_heels \| holding \| school_uniform \| white_footwear \| skindentation \| thighs \| outdoors \| bag \| sailor_hat \| crop_top \| from_behind \| red-framed_eyewear \| short_hair \| belt_pouch \| neckerchief \| panties \| under-rim_eyewear \| :d \| building \| midriff \| miniskirt \| sailor_shirt \| shoes \| large_breasts \| hairband \| white_bikini \| cleavage \| pink_skirt \| wet \| choker \| day \| hair_bow \| innertube \| bikini_skirt \| front-tie_top \| side-tie_bikini_bottom \| sky \| cloud \| beach \| pink_bikini \| water \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------\|:--------\|:---------\|:-------------\|:--------\|:----------\|:--------\|:--------\|:-------------\|:--------\|:-------------\|:----------\|:--------------\|:-------------\|:-----------------\|:------------------\|:-------\|:----------------\|:-------------\|:----------------\|:------------\|:-------------------\|:-------------\|:--------------------\|:-------\|:--------------\|:-------\|:------\|:---------\|:---------\|:-----------\|:------------\|:-------------------\|:----------------\|:--------------\|:-------\|:--------\|:----------------\|:----------------\|:--------------\|:----------------\|:---------------------\|:--------\|:---------------\|:------------\|:---------------\|:--------\|:-------------------------\|:--------------\|:--------------\|:-------------\|:----------\|:-----------------\|:-----------------\|:----------------\|:---------\|:-----------\|:------\|:-------------\|:-----------\|:--------------\|:---------------------\|:-------------\|:-------------\|:--------------\|:----------\|:--------------------\|:-----\|:-----------\|:----------\|:------------\|:---------------\|:--------\|:----------------\|:-----------\|:---------------\|:-----------\|:-------------\|:------\|:---------\|:------\|:-----------\|:------------\|:---------------\|:----------------\|:-------------------------\|:------\|:--------\|:--------\|:--------------\|:--------\| \| 0 \| 19 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| \| X \| \| \| X \| \| \| X \| \| \| X \| \| X \| X \| X \| \| \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 18 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 11 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| \| X \| \| X \| \| \| \| \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 19 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| X \| X \| \| \| \| \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
AdapterOcean/code_instructions_standardized_cluster_18_std	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 13708885 num_examples: 18198 download_size: 6325033 dataset_size: 13708885 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_18_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/okunoda_miyoi_touhou	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of okunoda_miyoi (Touhou) This is the dataset of okunoda_miyoi (Touhou), containing 242 images and their tags. The core tags of this character are `pink_hair, blue_headwear, hat, green_eyes, breasts, short_hair, bangs, hair_between_eyes, animal_hat, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 242 \| 313.99 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 242 \| 178.85 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 579 \| 397.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 242 \| 276.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 579 \| 575.82 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/okunoda_miyoi_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blue_shirt, looking_at_viewer, simple_background, solo, white_background, long_sleeves, purple_skirt, smile, blush, closed_mouth, fish_print, gourd, holding, white_shirt, standing \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blue_shirt, fish_print, looking_at_viewer, purple_skirt, short_sleeves, simple_background, solo, blue_apron, holding, open_mouth, :d, white_background, blush, cross-laced_clothes, ofuda_on_clothes \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blue_shirt, holding_tray, open_mouth, purple_skirt, solo, white_background, apron, fish_print, bottle, ofuda_on_clothes, short_sleeves, simple_background, tokkuri, gourd, looking_at_viewer, :d, blush, choko_(cup), full_body \| \| 3 \| 16 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blue_shirt, looking_at_viewer, solo, blush, short_sleeves, open_mouth, simple_background, white_background, upper_body, :d, purple_skirt, holding \| \| 4 \| 16 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blush, 1boy, hetero, solo_focus, nipples, penis, open_mouth, looking_at_viewer, paizuri, smile, upper_body, mosaic_censoring, sweat, cum_on_body, facial, huge_breasts, indoors, nude, one_eye_closed \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blue_shirt \| looking_at_viewer \| simple_background \| solo \| white_background \| long_sleeves \| purple_skirt \| smile \| blush \| closed_mouth \| fish_print \| gourd \| holding \| white_shirt \| standing \| short_sleeves \| blue_apron \| open_mouth \| :d \| cross-laced_clothes \| ofuda_on_clothes \| holding_tray \| apron \| bottle \| tokkuri \| choko_(cup) \| full_body \| upper_body \| 1boy \| hetero \| solo_focus \| nipples \| penis \| paizuri \| mosaic_censoring \| sweat \| cum_on_body \| facial \| huge_breasts \| indoors \| nude \| one_eye_closed \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------------\|:--------------------\|:--------------------\|:-------\|:-------------------\|:---------------\|:---------------\|:--------\|:--------\|:---------------\|:-------------\|:--------\|:----------\|:--------------\|:-----------\|:----------------\|:-------------\|:-------------\|:-----\|:----------------------\|:-------------------\|:---------------\|:--------\|:---------\|:----------\|:--------------\|:------------\|:-------------\|:-------\|:---------\|:-------------\|:----------\|:--------\|:----------\|:-------------------\|:--------\|:--------------\|:---------\|:---------------\|:----------\|:-------\|:-----------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| \| X \| X \| \| \| \| X \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 16 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| \| \| \| X \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 16 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
rdcoder/finetune-test-1	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 912758 num_examples: 404 download_size: 425762 dataset_size: 912758 configs: - config_name: default data_files: - split: train path: data/train-* ---
BangumiBase/punchline	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Punch Line This is the image base of bangumi Punch Line, we detected 17 characters, 1203 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 104 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 12 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 134 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 47 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 135 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 16 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 11 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 14 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 14 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 21 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 18 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 40 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| 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3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 9 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| noise \| 140 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
kobkrit/rd-taxqa	--- license: apache-2.0 ---
valituttiromero/Billy	--- license: openrail ---
eswardivi/IndicVoices	--- dataset_info: - config_name: telugu_train features: - name: audio dtype: audio - name: end dtype: float64 - name: text dtype: string - name: start dtype: float64 - name: speaker_id dtype: int64 - name: audio_raw_duration dtype: float64 - name: scenario dtype: string - name: task_name dtype: string splits: - name: split1 num_bytes: 1609730034.0 num_examples: 1000 - name: split2 num_bytes: 1049782097.0 num_examples: 1000 - name: split3 num_bytes: 1444386250.0 num_examples: 1000 - name: split4 num_bytes: 3152422807.0 num_examples: 1000 - name: split5 num_bytes: 5554666300.0 num_examples: 1000 - name: split6 num_bytes: 4003049531.0 num_examples: 500 - name: split7 num_bytes: 4053228693.0 num_examples: 500 - name: split8 num_bytes: 1044856061.0 num_examples: 344 - name: split9 num_bytes: 5059082700.0 num_examples: 500 - name: split10 num_bytes: 4664957526.0 num_examples: 500 download_size: 29946910673 dataset_size: 31636161999.0 - config_name: telugu_valid features: - name: audio dtype: audio - name: text dtype: string - name: audio_raw_duration dtype: float64 - name: scenario dtype: string - name: task_name dtype: string splits: - name: valid num_bytes: 314053800.0 num_examples: 73 download_size: 301914962 dataset_size: 314053800.0 configs: - config_name: telugu_train data_files: - split: split1 path: telugu_train/split1-* - split: split2 path: telugu_train/split2-* - split: split3 path: telugu_train/split3-* - split: split4 path: telugu_train/split4-* - split: split5 path: telugu_train/split5-* - split: split6 path: telugu_train/split6-* - split: split7 path: telugu_train/split7-* - split: split8 path: telugu_train/split8-* - split: split9 path: telugu_train/split9-* - split: split10 path: telugu_train/split10-* - config_name: telugu_valid data_files: - split: valid path: telugu_valid/valid-* --- This dataset is processed from [indicvoices](https://ai4bharat.iitm.ac.in/indicvoices/)
vikp/pypi_labeled	--- dataset_info: features: - name: code dtype: string - name: package dtype: string - name: path dtype: string - name: filename dtype: string - name: parsed_code dtype: string - name: quality_prob dtype: float64 - name: learning_prob dtype: float64 splits: - name: train num_bytes: 40005369487 num_examples: 1902405 download_size: 11174800633 dataset_size: 40005369487 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "pypi_labeled" All of the latest package versions from pypi. The original data came from [here](https://py-code.org/datasets). I pulled the latest versions of each package, then extracted only `md`, `rst`, `ipynb`, and `py` files. I then applied some cleaning: - rendering notebooks - removing leading comments/licenses Then filtered out some low-quality code, and labeled the rest according to learning value and quality. Subset by those columns to get higher quality code.
beenow011/quill	--- license: unknown ---
WillHeld/SD-QA	--- dataset_info: features: - name: id dtype: string - name: aus dtype: audio - name: gbr dtype: audio - name: ind_n dtype: audio - name: ind_s dtype: audio - name: irl dtype: audio - name: kenya dtype: audio - name: nga dtype: audio - name: nzl dtype: audio - name: phl dtype: audio - name: usa dtype: audio - name: zaf dtype: audio - name: answers sequence: string - name: question dtype: string splits: - name: dev num_bytes: 1605295564.0 num_examples: 1000 - name: test num_bytes: 1687465403.375 num_examples: 1031 download_size: 3028852311 dataset_size: 3292760967.375 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* ---
arnepeine/medspeech2	--- license: other ---
mteb/swerec_classification	--- language: - sv dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 355633 num_examples: 1024 - name: test num_bytes: 713970 num_examples: 2048 - name: val num_bytes: 82442 num_examples: 256 download_size: 697285 dataset_size: 1152045 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: val path: data/val-* ---
autoevaluate/autoeval-staging-eval-billsum-default-dd3eba-14585981	--- type: predictions tags: - autotrain - evaluation datasets: - billsum eval_info: task: summarization model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2 metrics: [] dataset_name: billsum dataset_config: default dataset_split: test col_mapping: text: text target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2 * Dataset: billsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
nibbz2024/SurveySense-datasetv1	--- task_categories: - text-generation language: - en ---
rntc/blurb_bc5disease_a-tm	--- dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: type dtype: string - name: ner_tags sequence: class_label: names: '0': O '1': B '2': I splits: - name: train num_bytes: 16267150 num_examples: 4560 - name: validation num_bytes: 15854894 num_examples: 4581 - name: test num_bytes: 16855285 num_examples: 4797 download_size: 6974702 dataset_size: 48977329 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
gilkeyio/librispeech-alignments	--- dataset_info: features: - name: sex dtype: string - name: subset dtype: string - name: id dtype: string - name: audio dtype: audio - name: transcript dtype: string - name: words list: - name: end dtype: float64 - name: start dtype: float64 - name: word dtype: string - name: phonemes list: - name: end dtype: float64 - name: phoneme dtype: string - name: start dtype: float64 splits: - name: dev_clean num_bytes: 365310608.879 num_examples: 2703 - name: dev_other num_bytes: 341143993.784 num_examples: 2864 - name: test_clean num_bytes: 377535532.98 num_examples: 2620 - name: test_other num_bytes: 351207892.569557 num_examples: 2938 - name: train_clean_100 num_bytes: 6694747231.610863 num_examples: 28538 - name: train_clean_360 num_bytes: 24163659711.787865 num_examples: 104008 - name: train_other_500 num_bytes: 32945085271.89443 num_examples: 148645 download_size: 62101682957 dataset_size: 65238690243.50571 configs: - config_name: default data_files: - split: dev_clean path: data/dev_clean-* - split: dev_other path: data/dev_other-* - split: test_clean path: data/test_clean-* - split: test_other path: data/test_other-* - split: train_clean_100 path: data/train_clean_100-* - split: train_clean_360 path: data/train_clean_360-* - split: train_other_500 path: data/train_other_500-* license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - en pretty_name: Librispeech Alignments size_categories: - 100K<n<1M --- # Dataset Card for Librispeech Alignments Librispeech with alignments generated by the [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/en/latest/). The original alignments in TextGrid format can be found [here](https://zenodo.org/records/2619474) ## Dataset Details ### Dataset Description Librispeech is a corpus of read English speech, designed for training and evaluating automatic speech recognition (ASR) systems. The dataset contains 1000 hours of 16kHz read English speech derived from audiobooks. The Montreal Forced Aligner (MFA) was used to generate word and phoneme level alignments for the Librispeech dataset. - Curated by: Vassil Panayotov, Guoguo Chen, Daniel Povey, Sanjeev Khudanpur (for Librispeech) - Funded by: DARPA LORELEI - Shared by: Loren Lugosch (for Alignments) - Language(s) (NLP): English - License: Creative Commons Attribution 4.0 International License ### Dataset Sources - Repository: https://www.openslr.org/12 - Paper: https://arxiv.org/abs/1512.02595 - Alignments: https://zenodo.org/record/2619474 ## Uses ### Direct Use The Librispeech dataset can be used to train and evaluate ASR systems. The alignments allow for forced alignment techniques. ### Out-of-Scope Use The dataset only contains read speech, so may not perform as well on spontaneous conversational speech. ## Dataset Structure The dataset contains 1000 hours of segmented read English speech from audiobooks. There are three train subsets: 100 hours (train-clean-100), 360 hours (train-clean-360) and 500 hours (train-other-500). The alignments connect the audio to the reference text transcripts on word and phoneme level. ### Data Fields - sex: M for male, F for female - subset: dev_clean, dev_other, test_clean, test_other, train_clean_100, train_clean_360, train_other_500 - id: unique id of the data sample. (speaker id)-(chapter-id)-(utterance-id) - audio: the audio, 16kHz - transcript: the spoken text of the dataset, normalized and lowercased - words: a list of words with fields: - word: the text of the word - start: the start time in seconds - end: the end time in seconds - phonemes: a list of phonemes with fields: - phoneme: the phoneme spoken - start: the start time in seconds - end: the end time in seconds ## Dataset Creation ### Curation Rationale Librispeech was created to further speech recognition research and to benchmark progress in the field. ### Source Data #### Data Collection and Processing The audio and reference texts were sourced from read English audiobooks in the LibriVox project. The data was segmented, filtered and prepared for speech recognition. #### Who are the source data producers? The audiobooks are read by volunteers for the LibriVox project. Information about the readers is available in the LibriVox catalog. ### Annotations #### Annotation process The Montreal Forced Aligner was used to create word and phoneme level alignments between the audio and reference texts. The aligner is based on Kaldi. In the process of formatting this into a HuggingFace dataset, words with empty text and phonemes with empty text, silence tokens, or spacing tokens were removed #### Who are the annotators? The alignments were generated automatically by the Montreal Forced Aligner and shared by Loren Lugosch. The TextGrid files were parsed and integrated into this dataset by Kim Gilkey. #### Personal and Sensitive Information The data contains read speech and transcripts. No personal or sensitive information expected. ## Bias, Risks, and Limitations The dataset contains only read speech from published books, not natural conversational speech. Performance on other tasks may be reduced. ### Recommendations Users should understand that the alignments may contain errors and account for this in applications. For example, be wary of <UNK> tokens. ## Citation Librispeech: ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={ICASSP}, year={2015}, organization={IEEE} } ``` Librispeech Alignments: ``` Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, "Speech Model Pre-training for End-to-End Spoken Language Understanding", Interspeech 2019. ``` Montreal Forced Aligner: ``` Michael McAuliffe, Michaela Socolof, Sarah Mihuc, Michael Wagner, and Morgan Sonderegger. "Montreal Forced Aligner: trainable text-speech alignment using Kaldi", Interspeech 2017. ```
yzhuang/autotree_automl_california_sgosdt_l256_d3_sd0	--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 185200000 num_examples: 10000 - name: validation num_bytes: 185200000 num_examples: 10000 download_size: 149978405 dataset_size: 370400000 --- # Dataset Card for "autotree_automl_california_sgosdt_l256_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Seongill/SQuAD_unique_questions	--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: masked_query dtype: string - name: query_embedding sequence: float64 splits: - name: train num_bytes: 697771735 num_examples: 97888 download_size: 506779114 dataset_size: 697771735 configs: - config_name: default data_files: - split: train path: data/train-* ---
Yashaswat/Indian-Legal-Text-ABS	--- dataset_info: features: - name: judgement dtype: string - name: summary dtype: string splits: - name: train num_bytes: 106644414 num_examples: 3599 - name: test num_bytes: 3260335 num_examples: 100 - name: validation num_bytes: 26921474 num_examples: 900 download_size: 66712543 dataset_size: 136826223 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
njkumarr/humanevalinfilljava	--- language: - en pretty_name: HumanEval-Infilling Java --- Dataset for human eval infill for java, based on https://arxiv.org/pdf/2207.14255
mesolitica/mixtral-malaysian-rag	--- license: mit language: - ms --- # Mixtral Malaysian RAG Simulate Multi-turn RAG conversations, some questions purposely asked out-of-context and the users throw random rubbish replies. notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/mixtral-malaysian-rag - [rag-multiturn-chaotic.jsonl](rag-multiturn-chaotic.jsonl), 327980 rows, 4.1 GB - [rag-multiturn-chaotic-part2.jsonl](rag-multiturn-chaotic-part2.jsonl), 169403 rows, 2.45 GB - [mixtral-rag-question-disagree.jsonl](mixtral-rag-question-disagree.jsonl), 339627 rows, 3.08 GB ## Example data ```python {'filename': 'gov.my.jsonl', 'chat': [{'role': 'user', 'content': "Are you as sure as a horse's hind leg?", 'content_ms': 'Adakah anda pasti seperti kaki belakang kuda?'}, {'role': 'assistant', 'content': "I'm afraid I don't understand the reference to a horse's hind leg in relation to my ability to provide accurate information. However, I can assure you that I will provide information based solely on the knowledge base you've given me and to the best of my ability, I will provide accurate and relevant responses. I don't have feelings, beliefs, or personal knowledge, I am a machine learning model that generate text based on the input data.", 'content_ms': 'Saya takut saya tidak memahami rujukan kepada kaki belakang kuda berkaitan dengan keupayaan saya untuk memberikan maklumat yang tepat. Walau bagaimanapun, saya boleh memberi jaminan kepada anda bahawa saya akan memberikan maklumat hanya berdasarkan pangkalan pengetahuan yang anda berikan kepada saya dan setakat kemampuan saya, saya akan memberikan respons yang tepat dan relevan. Saya tidak mempunyai perasaan, kepercayaan atau pengetahuan peribadi, saya ialah model pembelajaran mesin yang menjana teks berdasarkan data input.'}, {'role': 'user', 'content': 'When does the order come into force?', 'content_ms': 'Bilakah perintah itu mula berkuat kuasa?'}, {'role': 'assistant', 'content': 'Based on the knowledge base provided, the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989 comes into force on the 1st of May, 1989.', 'content_ms': 'Berdasarkan pangkalan pengetahuan yang disediakan, Perintah Kualiti Alam Sekeliling (Premis Ditetapkan) (Kemudahan Rawatan dan Pelupusan Sisa Terjadual) 1989 berkuat kuasa pada 1 Mei 1989.'}, {'role': 'user', 'content': 'How does the order impact businesses and individuals in Malaysia?', 'content_ms': 'Bagaimanakah pesanan itu memberi kesan kepada perniagaan dan individu di Malaysia?'}, {'role': 'assistant', 'content': 'Based on the knowledge base provided, the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989, which comes into force on the 1st of May, 1989 in Malaysia, is a subsidiary legislation under the Environmental Quality Act 1974 [Act 127]. This order sets forth the regulations for prescribed premises and scheduled wastes treatment and disposal facilities in Malaysia. The aim of this order is to control and manage the generation, treatment, storage, and disposal of scheduled wastes in a manner that protects the environment and human health.\n\nTherefore, businesses and individuals in Malaysia that generate, handle, or dispose of scheduled wastes, as defined in the order, will be impacted by these regulations. They will be required to comply with the standards and requirements set forth in the order, such as proper treatment, storage, and disposal of scheduled wastes, and obtaining the necessary permits and approvals from the relevant authorities. Non-compliance with the regulations can result in penalties, such as fines and/or imprisonment. It is important for businesses and individuals in Malaysia to be aware of and understand the requirements of this order, and to take appropriate measures to ensure compliance.', 'content_ms': 'Berdasarkan pangkalan pengetahuan yang disediakan, Perintah Kualiti Alam Sekeliling (Premis Ditetapkan) (Kemudahan Rawatan dan Pelupusan Sisa Terjadual) 1989, yang berkuat kuasa pada 1 Mei, 1989 di Malaysia, adalah perundangan subsidiari di bawah Akta Kualiti Alam Sekitar 1974 [ Akta 127]. Perintah ini menetapkan peraturan untuk premis yang ditetapkan dan kemudahan rawatan dan pelupusan sisa terjadual di Malaysia. Matlamat perintah ini adalah untuk mengawal dan mengurus penjanaan, rawatan, penyimpanan dan pelupusan sisa terjadual dengan cara yang melindungi alam sekitar dan kesihatan manusia.\n\nOleh itu, perniagaan dan individu di Malaysia yang menjana, mengendalikan atau membuang sisa terjadual, seperti yang dinyatakan dalam perintah itu, akan terjejas oleh peraturan ini. Mereka akan dikehendaki mematuhi piawaian dan keperluan yang ditetapkan dalam perintah itu, seperti rawatan yang betul, penyimpanan, dan pelupusan sisa terjadual, dan mendapatkan permit dan kelulusan yang diperlukan daripada pihak berkuasa yang berkaitan. Ketidakpatuhan terhadap peraturan boleh mengakibatkan penalti, seperti denda dan/atau penjara. Adalah penting bagi perniagaan dan individu di Malaysia untuk menyedari dan memahami keperluan perintah ini, dan mengambil langkah yang sesuai untuk memastikan pematuhan.'}, {'role': 'user', 'content': 'What are the potential consequences for non-compliance with the order?', 'content_ms': 'Apakah akibat yang berpotensi untuk ketidakpatuhan terhadap perintah itu?'}, {'role': 'assistant', 'content': "Based on the knowledge base provided, the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989 is a subsidiary legislation under the Environmental Quality Act 1974 in Malaysia. The order sets forth the regulations for prescribed premises and scheduled wastes treatment and disposal facilities in Malaysia.\n\nNon-compliance with the regulations set forth in the order can result in penalties, such as fines and/or imprisonment. The exact penalties for non-compliance are not specified in the order, but are likely defined in the Environmental Quality Act 1974.\n\nIt's important to note that I'm a language model, and my knowledge cut off is 2021, I don't have real-time access to any laws, regulations or any other legal information. It is always recommended to consult the legal text or consult with a legal professional to get the most accurate and up-to-date information about the potential consequences of non-compliance with any specific regulation.", 'content_ms': 'Berdasarkan pangkalan pengetahuan yang disediakan, Perintah Kualiti Alam Sekeliling (Premis Ditetapkan) (Kemudahan Rawatan dan Pelupusan Sisa Terjadual) 1989 adalah perundangan subsidiari di bawah Akta Kualiti Alam Sekeliling 1974 di Malaysia. Perintah itu menetapkan peraturan untuk premis yang ditetapkan dan kemudahan rawatan dan pelupusan sisa terjadual di Malaysia.\n\nKetidakpatuhan terhadap peraturan yang ditetapkan dalam perintah itu boleh mengakibatkan penalti, seperti denda dan/atau penjara. Penalti yang tepat bagi ketidakpatuhan tidak dinyatakan dalam perintah itu, tetapi mungkin ditakrifkan dalam Akta Kualiti Alam Sekeliling 1974.\n\nAdalah penting untuk ambil perhatian bahawa saya adalah model bahasa, dan pengetahuan saya terputus ialah 2021, saya tidak mempunyai akses masa nyata kepada mana-mana undang-undang, peraturan atau sebarang maklumat undang-undang lain. Ia sentiasa disyorkan untuk merujuk teks undang-undang atau berunding dengan profesional undang-undang untuk mendapatkan maklumat yang paling tepat dan terkini tentang kemungkinan akibat ketidakpatuhan terhadap mana-mana peraturan tertentu.'}, {'role': 'user', 'content': "How does the order contribute to Malaysia's overall environmental quality and sustainability efforts?", 'content_ms': 'Bagaimanakah perintah itu menyumbang kepada usaha kualiti alam sekitar dan kemampanan keseluruhan Malaysia?'}, {'role': 'assistant', 'content': "Based on the provided knowledge base, the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989 is a subsidiary legislation under the Environmental Quality Act 1974 in Malaysia. The order sets forth the regulations for prescribed premises and scheduled wastes treatment and disposal facilities in Malaysia, which aim to control and manage the generation, treatment, storage, and disposal of scheduled wastes in a manner that protects the environment and human health. By establishing standards and requirements for the proper handling and disposal of scheduled wastes, the order helps to prevent pollution, protect the environment, and promote sustainability. This order is one of the regulations put in place by the Malaysian government to control and manage the waste generated by different industries and businesses, in order to achieve a good environmental quality and a sustainable development.\n\nAdditionally, The Environmental Quality Act 1974 is a primary legislation that provides the legal framework for environmental protection, conservation and management in Malaysia, which aim to protect and improve the quality of the environment, and to provide for the prevention, abatement and control of pollution and environmental degradation. The order contributes to Malaysia's overall environmental quality and sustainability efforts by setting specific regulations that must be followed, thus ensuring a better and more effective enforcement of the general objectives of the act.\n\nHowever, I'd like to point out again that my knowledge cut off is 2021 and I am a machine learning model, and my response is based on the provided knowledge base, and I do not have real-time access to any laws, regulations or any other legal information. It is always recommended to consult the legal text or consult with a legal professional to get the most accurate and up-to-date information about the impact of the order on Malaysia's overall environmental quality and sustainability efforts.", 'content_ms': 'Berdasarkan pangkalan pengetahuan yang disediakan, Perintah Kualiti Alam Sekeliling (Premis Ditetapkan) (Kemudahan Rawatan dan Pelupusan Sisa Terjadual) 1989 ialah perundangan subsidiari di bawah Akta Kualiti Alam Sekeliling 1974 di Malaysia. Perintah itu menetapkan peraturan untuk premis yang ditetapkan dan kemudahan rawatan dan pelupusan sisa terjadual di Malaysia, yang bertujuan untuk mengawal dan mengurus penjanaan, rawatan, penyimpanan, dan pelupusan sisa terjadual dengan cara yang melindungi alam sekitar dan kesihatan manusia. Dengan mewujudkan piawaian dan keperluan untuk pengendalian dan pelupusan sisa terjadual yang betul, perintah itu membantu mencegah pencemaran, melindungi alam sekitar dan menggalakkan kemampanan. Perintah ini adalah salah satu peraturan yang ditetapkan oleh kerajaan Malaysia untuk mengawal dan mengurus sisa yang dihasilkan oleh industri dan perniagaan yang berbeza, untuk mencapai kualiti alam sekitar yang baik dan pembangunan yang mampan.\n\nDi samping itu, Akta Kualiti Alam Sekeliling 1974 ialah perundangan utama yang menyediakan rangka kerja perundangan untuk perlindungan, pemuliharaan dan pengurusan alam sekitar di Malaysia, yang bertujuan untuk melindungi dan meningkatkan kualiti alam sekitar, dan untuk memperuntukkan pencegahan, pengurangan dan kawalan pencemaran. dan kemerosotan alam sekitar. Perintah itu menyumbang kepada keseluruhan usaha kualiti alam sekitar dan kemampanan Malaysia dengan menetapkan peraturan khusus yang mesti dipatuhi, sekali gus memastikan penguatkuasaan yang lebih baik dan berkesan terhadap objektif am akta tersebut.\n\nWalau bagaimanapun, saya ingin menegaskan sekali lagi bahawa pengetahuan saya terputus ialah 2021 dan saya adalah model pembelajaran mesin, dan respons saya adalah berdasarkan pangkalan pengetahuan yang disediakan, dan saya tidak mempunyai akses masa nyata kepada mana-mana undang-undang, peraturan. atau sebarang maklumat undang-undang lain. Ia sentiasa disyorkan untuk merujuk teks undang-undang atau berunding dengan profesional undang-undang untuk mendapatkan maklumat yang paling tepat dan terkini tentang kesan perintah itu terhadap keseluruhan usaha kualiti alam sekitar dan kelestarian Malaysia.'}], 'context': 'and Chen, J (2013). Integration of high-resolution physical and genetic map reveals differential recombination frequency between chromosomes and the genome assembling quality in cucumber. PLOS ONE 8(5): e62676. Madon, M; Arulandoo, X; Sritharan, K; Nordiana, H M N; Muhammad Azwan, Z and Zaki, N M (2018). Short communication: Genomic constitution of oil palm interspecific hybrid crosses monitored by genomic in situ hybridization (GISH). J. Oil Palm Res. Vol. 30(2): 338-344. Madon, M; Clyde, M M and Cheah, S C (1999). Application of genomic in situ hybridization (GISH) on Elaeis hybrids. J. Oil Palm Res. Vol. (Special Issue): 74-80. Madon, M; Heslop-Harrison, J S; Schwarzacher, T; Mohd Rafdi, M H and Clyde, M M (2005). Cytological analysis of oil palm pollen mother cells (PMC). J. Oil Palm Res. Vol. 17: 176-180. Maluszynska, J and Hasterok, R (2005). Identification of individual chromosomes and parental genomes in Brassica juncea using GISH and FISH. Cyto. and Genome Res. 109: 310-314. Meunier, J (1987). Prospects of E. oleifera x E. guineensis interspecific hybrids. IRHO results. Proc. of ISOPB/PORIM Workshop on Prospects of Interspecific Hybrids (Soh, A C and Rajanaidu, N eds.). p. 11-15. Murphy, S P and Bass, H W (2012). Genetics and cytology of meiotic chromosome behavior in plants. Plant Cytogenetics, Plant Genetics and Genomics: Crops and Models 4 (Bass, H W and Birchler, J A eds.). 345 pp. DOI:10.1007/978-0-387-70869-0_8. Nair, P K K (1969). The thesis of pollen based plant research. J. Palynol., 5(1): 49-50. Nordiana, H M N; Muhammad Azwan, Z; Arulandoo, X; Sritharan, K and Madon, M (2016). Development of meiotic pachytene chromosomes preparation technique in oil palm interspecific hybrid and backcross (BC2), Transactions of Persatuan Genetik Malaysia Vol. 3. Genetics Society of Malaysia. p. 143-146. Phillips, D; Wnetrzak, J; Nibau, C; Barakate, A; Ramsay, L; Wright, F; Higgins, J D, Perry, R M and Jenkins, G (2013). Quantitative high resolution mapping of HvMLH3 foci in barley pachytene nuclei reveals a strong distal bias and weak interference. J. Exp. Bot., 64(8): 2139-2154. DOI:10.1093/jxb/ert079. Rahimah, A B; Cheah, S C and Singh, R (2006). Freeze-drying of oil palm (Elaeis guineensis) leaf and its effect on the quality of extractable DNA. J. Oil Palm Res. Vol. 18: 296-304. Rajanaidu, N; Chin, C W and Jalani, B S (1995). Performance of Elaeis oleifera (Surinam) x E. guineensis hybrids. ISOPB International Seminar. Barranquilla, Colombia. Rasheed, A A; Perveen, A; Abid, R and Qaiser, M (2016). Pollen morphology of the subfamily Arecoideae Griff. (family-Arecaceae) from Pakistan and Kashmir. Pak. J. Bot., 48(3): 1051-1060. Ravi Kumar, C and Nair, P K K (1986). Inheritance of exine ornamentation and pollen shape in the interspecific tetraploid hybrids of Gloriosa. Can. J. Bot., 64: 3134-3140. Rhee, H K; Cho, H R and Kim, K S (2005). Comparison of pollen morphology in interspecific hybrid lilies after in vitro chromosome doubling. Proc. of the IXth International Symposium on Flower Bulbs. Acta Hot., 673: 639-643. Schubert, I; Fransz, P F; Fuchs, J and De Jong, J H (2001). Chromosome painting in plants. Methods in Cell Science, 23(1-3): 57-69. Schwarzacher, T and Heslop-Harrison, J S (2000). Practical in situ hybridization. BIOS Scientific Publishers Ltd, Abindgdon, United Kingdom. 250 pp. Schwarzacher, T; Anamthawat-Jonsson, K and Harrison, G E (1992). Genomic in situ hybridization to identify alien chromosomes and chromosome segments in wheat. Theor. Appl. Genet., 84: 778-786. Singh, R; Ong-Abdullah, M; Low, E T L; Manaf, M A A; Rosli, R; Rajanaidu, N; Ooi, L C L; Ooi, S E; Chan, K-L; Ab Halim, M A; Azizi, N; Jayanthi, N; Bacher, B; Lakey, N; Steven, W S; He, D; Hogan, M; Budiman, M A; Lee, E K; Desalle, R; Kudma, D; Goicoechea, J L; Wing, R A; Wilson, R K; Fulton, R S; Ordway, J M; 11 OIL PALM MALE MEIOSIS PROFILING: FROM FIELD OBSERVATION TO CYTOGENETICS ANALYSIS Martienssen, R A and Sambanthamurthi, R (2013). Oil palm genome sequence reveals divergence of interfertile species in Old and New worlds. Nature, 500: 335-339. Thomas, P T and Thomas, H (1973). Annual Report of the Welsh Plant Breeding Station for 1973. Aberystwyth, Cambrian News. 85 pp. Zhong, X B; De Jong, J H and Zabel, P (1996). Preparation of tomato meiotic pachytene and mitotic metaphase chromosomes suitable for fluorescence in situ hybridization (FISH). Chromosome Res., 4: 24-28. FEDERAL SUBSIDIARY LEGISLATION ENVIRONMENTAL QUALITY ACT 1974 [ACT 127] P.U.(A) 140/89 ENVIRONMENTAL QUALITY (PRESCRIBED PREMISES) (SCHEDULED WASTES TREATMENT AND DISPOSAL FACILITIES) ORDER 1989 Incorporating latest amendments - P.U.(A) 252/2006 Publication : 27th April 1989 Date of coming into operation : 1st May 1989 ARRANGEMENT OF ORDERS Preamble Order 1. Citation and commencement. Order 2. Interpretation. Order 3. Prescribed premises. LIST OF AMENDMENTS. Preamble IN exercise of the powers conferred by section 18 of the Environmental Quality Act 1974 [Act 127], the Minister, after consultation with the Environmental Quality Council, makes the following order : Order 1. Citation and commencement. This order may be cited as the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989 and shall come into force on the 1st May 1989.', 'questions': ['What is the purpose of the Environmental Quality Act 1974 and the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989?', 'What is the role of the Minister and the Environmental Quality Council in creating the order?', 'What premises are prescribed in the order?', 'When does the order come into force?', 'How are terms in the order defined in Order 2?', 'What is the significance of the amendments listed in the LIST OF AMENDMENTS section?', 'What other federal subsidiary legislation relates to environmental quality in Malaysia?', 'How does the Environmental Quality Act 1974 relate to other environmental legislation in Malaysia?', 'How does the order fit into the broader context of environmental regulations in Malaysia?', 'How does the order compare to similar regulations in other countries?', 'How is compliance with the order enforced?', 'What are the potential consequences for non-compliance with the order?', 'Are there any notable court cases or legal precedents related to the order?', 'How does the order impact businesses and individuals in Malaysia?', "How does the order contribute to Malaysia's overall environmental quality and sustainability efforts?"]} ```
ZahraAlharz/alriyadh3	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: content dtype: string - name: url dtype: string - name: date dtype: string - name: title dtype: string splits: - name: train num_bytes: 6387 num_examples: 3 download_size: 20826 dataset_size: 6387 --- # Dataset Card for "alriyadh3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
synthseq/circuits	--- license: mit ---
RJuro/go-emotion-dk-autotranlated-10k	--- dataset_info: features: - name: text_en dtype: string - name: text dtype: string - name: labels dtype: class_label: names: '0': admiration '1': amusement '2': anger '3': annoyance '4': approval '5': caring '6': confusion '7': curiosity '8': desire '9': disappointment '10': disapproval '11': disgust '12': embarrassment '13': excitement '14': fear '15': gratitude '16': grief '17': joy '18': love '19': nervousness '20': neutral '21': optimism '22': pride '23': realization '24': relief '25': remorse '26': sadness '27': surprise - name: __index_level_0__ dtype: int64 - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 2916184 num_examples: 9000 - name: test num_bytes: 159317 num_examples: 500 - name: valid num_bytes: 162379 num_examples: 500 download_size: 1662215 dataset_size: 3237880 --- # Dataset Card for "go-emotion-dk-autotranlated-10k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
VivekNaga/Medicaldata	--- license: apache-2.0 ---
grakky1510/growheads	--- license: apache-2.0 ---
nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io	--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m1_fine_tuning_ocr_ptrn_cmbert_io ## Introduction This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for nested NER task using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century. ## Dataset parameters * Approach : M1 * Dataset type : noisy (Pero OCR) * Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) * Tagging format : IO * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned models : * Level-1 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1) * Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2) ## Entity types Abbreviation\|Entity group (level)\|Description -\|-\|- O \|1 & 2\|Outside of a named entity PER \|1\|Person or company name ACT \|1 & 2\|Person or company professional activity TITREH \|2\|Military or civil distinction DESC \|1\|Entry full description TITREP \|2\|Professionnal reward SPAT \|1\|Address LOC \|2\|Street name CARDINAL \|2\|Street number FT \|2\|Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io")
i2/cnn_models	--- dataset_info: features: - name: code dtype: string splits: - name: train num_bytes: 2708721.9 num_examples: 4500 - name: test num_bytes: 300969.1 num_examples: 500 download_size: 61959 dataset_size: 3009691.0 --- # Dataset Card for "cnn_models" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1NightRaid1/testing	--- license: apache-2.0 task_categories: - question-answering tags: - code size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
Paulitos/school-math-questions-llama2-5k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2721591 num_examples: 5000 download_size: 1363904 dataset_size: 2721591 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/ticonderoga_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ticonderoga/タイコンデロガ/提康德罗加 (Azur Lane) This is the dataset of ticonderoga/タイコンデロガ/提康德罗加 (Azur Lane), containing 55 images and their tags. The core tags of this character are `blue_eyes, breasts, long_hair, large_breasts, blue_hair, hair_ornament, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 55 \| 81.47 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 55 \| 44.72 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 139 \| 97.69 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 55 \| 71.26 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 139 \| 139.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/ticonderoga_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, blue_bikini, cleavage, looking_at_viewer, smile, thigh_strap, o-ring_bikini, eyewear_on_head, sunglasses, beach, navel, skindentation, bare_shoulders, collarbone, outdoors, sitting, blush, closed_mouth, day, open_clothes, see-through, alternate_costume, black_choker, bracelet, food, stomach, thighs \| \| 1 \| 15 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, fingerless_gloves, looking_at_viewer, elbow_gloves, solo, red_necktie, coat_on_shoulders, black_thighhighs, skirt, smile, dress, open_coat, simple_background \| \| 2 \| 16 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, looking_at_viewer, solo, fake_animal_ears, playboy_bunny, rabbit_ears, detached_collar, official_alternate_costume, wrist_cuffs, cleavage, strapless_leotard, two-tone_legwear, very_long_hair, bare_shoulders, red_bowtie, navel_cutout, striped_thighhighs, purple_thighhighs, white_background, black_footwear, cup, high_heels, simple_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| blue_bikini \| cleavage \| looking_at_viewer \| smile \| thigh_strap \| o-ring_bikini \| eyewear_on_head \| sunglasses \| beach \| navel \| skindentation \| bare_shoulders \| collarbone \| outdoors \| sitting \| blush \| closed_mouth \| day \| open_clothes \| see-through \| alternate_costume \| black_choker \| bracelet \| food \| stomach \| thighs \| fingerless_gloves \| elbow_gloves \| red_necktie \| coat_on_shoulders \| black_thighhighs \| skirt \| dress \| open_coat \| simple_background \| fake_animal_ears \| playboy_bunny \| rabbit_ears \| detached_collar \| official_alternate_costume \| wrist_cuffs \| strapless_leotard \| two-tone_legwear \| very_long_hair \| red_bowtie \| navel_cutout \| striped_thighhighs \| purple_thighhighs \| white_background \| black_footwear \| cup \| high_heels \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------\|:-----------\|:--------------------\|:--------\|:--------------\|:----------------\|:------------------\|:-------------\|:--------\|:--------\|:----------------\|:-----------------\|:-------------\|:-----------\|:----------\|:--------\|:---------------\|:------\|:---------------\|:--------------\|:--------------------\|:---------------\|:-----------\|:-------\|:----------\|:---------\|:--------------------\|:---------------\|:--------------\|:--------------------\|:-------------------\|:--------\|:--------\|:------------\|:--------------------\|:-------------------\|:----------------\|:--------------\|:------------------\|:-----------------------------\|:--------------\|:--------------------\|:-------------------\|:-----------------\|:-------------\|:---------------\|:---------------------\|:--------------------\|:-------------------\|:-----------------\|:------\|:-------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 15 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 16 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
AnyaSaphe/killjoy	--- license: openrail ---
CyberHarem/amakusa_shino_seitokaiyakuindomo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Amakusa Shino (Seitokai Yakuindomo) This is the dataset of Amakusa Shino (Seitokai Yakuindomo), containing 972 images and their tags. The core tags of this character are `long_hair, black_hair, brown_eyes, bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 972 \| 479.25 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 972 \| 410.84 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1920 \| 764.81 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 972 \| 478.94 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1920 \| 869.95 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/amakusa_shino_seitokaiyakuindomo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blazer, school_uniform, solo, upper_body, purple_eyes \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blazer, school_uniform, solo \| \| 2 \| 12 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blazer, school_uniform, solo, profile, black_eyes \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, armband, blazer, school_uniform, solo, smile \| \| 4 \| 18 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, school_uniform, solo, blazer, plaid_skirt \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, blazer, bowtie, hair_between_eyes, looking_at_viewer, school_uniform, solo, upper_body, red_bow \| \| 6 \| 8 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, parody, school_uniform, solo, anime_coloring \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, chair, desk, school_uniform, sitting, solo, classroom, profile \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blazer \| school_uniform \| solo \| upper_body \| purple_eyes \| profile \| black_eyes \| armband \| smile \| plaid_skirt \| bowtie \| hair_between_eyes \| looking_at_viewer \| red_bow \| parody \| anime_coloring \| chair \| desk \| sitting \| classroom \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------\|:-----------------\|:-------\|:-------------\|:--------------\|:----------\|:-------------\|:----------\|:--------\|:--------------\|:---------\|:--------------------\|:--------------------\|:----------\|:---------\|:-----------------\|:--------\|:-------\|:----------\|:------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 12 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 18 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| X \| X \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| 6 \| 8 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \|
syeddhasnainn/landslide-segmentation-dataset-512-512	--- dataset_info: features: - name: image dtype: image - name: annotation dtype: image splits: - name: train num_bytes: 1244980500.0 num_examples: 1187 - name: validation num_bytes: 311507338.0 num_examples: 297 download_size: 1162324382 dataset_size: 1556487838.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
heliosprime/twitter_dataset_1713022808	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 12086 num_examples: 28 download_size: 8949 dataset_size: 12086 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713022808" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_core-3__kuno-dogpark-7b	--- pretty_name: Evaluation run of core-3/kuno-dogpark-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [core-3/kuno-dogpark-7b](https://huggingface.co/core-3/kuno-dogpark-7b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_core-3__kuno-dogpark-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-03T22:15:40.975282](https://huggingface.co/datasets/open-llm-leaderboard/details_core-3__kuno-dogpark-7b/blob/main/results_2024-03-03T22-15-40.975282.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\": 0.6561091309021897,\n\ \ \"acc_stderr\": 0.03208260740047248,\n \"acc_norm\": 0.6557428188238892,\n\ \ \"acc_norm_stderr\": 0.032748123628975445,\n \"mc1\": 0.5569155446756426,\n\ \ \"mc1_stderr\": 0.017389730346877116,\n \"mc2\": 0.7113693226923901,\n\ \ \"mc2_stderr\": 0.014768500069791109\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6962457337883959,\n \"acc_stderr\": 0.013438909184778764,\n\ \ \"acc_norm\": 0.7184300341296929,\n \"acc_norm_stderr\": 0.013143376735009024\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.710017924716192,\n\ \ \"acc_stderr\": 0.004528264116475879,\n \"acc_norm\": 0.8814977096195977,\n\ \ \"acc_norm_stderr\": 0.0032254141192897155\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933713,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933713\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7847222222222222,\n\ \ \"acc_stderr\": 0.03437079344106136,\n \"acc_norm\": 0.7847222222222222,\n\ \ \"acc_norm_stderr\": 0.03437079344106136\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878151,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878151\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083522,\n \"\ acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083522\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4827586206896552,\n \"acc_stderr\": 0.035158955511657,\n \"acc_norm\"\ : 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511657\n },\n\ \ \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\"\ : 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \ \ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857413,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857413\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.03006676158297793,\n \ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.03006676158297793\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659807,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659807\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\ : 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\n },\n\ \ \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\":\ \ 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \"acc_norm\"\ : 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n },\n\ \ \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281365,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281365\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8237547892720306,\n\ \ \"acc_stderr\": 0.013625556907993462,\n \"acc_norm\": 0.8237547892720306,\n\ \ \"acc_norm_stderr\": 0.013625556907993462\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.02370309952525818,\n\ \ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.02370309952525818\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4346368715083799,\n\ \ \"acc_stderr\": 0.01657899743549672,\n \"acc_norm\": 0.4346368715083799,\n\ \ \"acc_norm_stderr\": 0.01657899743549672\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.0242885336377261,\n\ \ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.0242885336377261\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.470013037809648,\n\ \ \"acc_stderr\": 0.012747248967079069,\n \"acc_norm\": 0.470013037809648,\n\ \ \"acc_norm_stderr\": 0.012747248967079069\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.02824568739146293,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.02824568739146293\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7510204081632653,\n \"acc_stderr\": 0.027682979522960234,\n\ \ \"acc_norm\": 0.7510204081632653,\n \"acc_norm_stderr\": 0.027682979522960234\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.02709729011807081,\n\ \ \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.02709729011807081\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5569155446756426,\n\ \ \"mc1_stderr\": 0.017389730346877116,\n \"mc2\": 0.7113693226923901,\n\ \ \"mc2_stderr\": 0.014768500069791109\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8224151539068666,\n \"acc_stderr\": 0.010740676861359238\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7050796057619408,\n \ \ \"acc_stderr\": 0.012560698010954767\n }\n}\n```" repo_url: https://huggingface.co/core-3/kuno-dogpark-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|arc:challenge\|25_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-03T22-15-40.975282.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|gsm8k\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hellaswag\|10_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-03T22-15-40.975282.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-03T22-15-40.975282.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '/details_harness\|winogrande\|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-03T22-15-40.975282.parquet' - config_name: results data_files: - split: 2024_03_03T22_15_40.975282 path: - results_2024-03-03T22-15-40.975282.parquet - split: latest path: - results_2024-03-03T22-15-40.975282.parquet --- # Dataset Card for Evaluation run of core-3/kuno-dogpark-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [core-3/kuno-dogpark-7b](https://huggingface.co/core-3/kuno-dogpark-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_core-3__kuno-dogpark-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-03T22:15:40.975282](https://huggingface.co/datasets/open-llm-leaderboard/details_core-3__kuno-dogpark-7b/blob/main/results_2024-03-03T22-15-40.975282.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": 0.6561091309021897, "acc_stderr": 0.03208260740047248, "acc_norm": 0.6557428188238892, "acc_norm_stderr": 0.032748123628975445, "mc1": 0.5569155446756426, "mc1_stderr": 0.017389730346877116, "mc2": 0.7113693226923901, "mc2_stderr": 0.014768500069791109 }, "harness\|arc:challenge\|25": { "acc": 0.6962457337883959, "acc_stderr": 0.013438909184778764, "acc_norm": 0.7184300341296929, "acc_norm_stderr": 0.013143376735009024 }, "harness\|hellaswag\|10": { "acc": 0.710017924716192, "acc_stderr": 0.004528264116475879, "acc_norm": 0.8814977096195977, "acc_norm_stderr": 0.0032254141192897155 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7094339622641509, "acc_stderr": 0.02794321998933713, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933713 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106136, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106136 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878151, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878151 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406783, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406783 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083522, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083522 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511657, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511657 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.676923076923077, "acc_stderr": 0.02371088850197057, "acc_norm": 0.676923076923077, "acc_norm_stderr": 0.02371088850197057 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.35555555555555557, "acc_stderr": 0.029185714949857413, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.029185714949857413 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6890756302521008, "acc_stderr": 0.03006676158297793, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.03006676158297793 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.39072847682119205, "acc_stderr": 0.03983798306659807, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.03983798306659807 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.0251956584289318, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.0251956584289318 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.035477710041594654, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.035477710041594654 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281365, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281365 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993462, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993462 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7369942196531792, "acc_stderr": 0.02370309952525818, "acc_norm": 0.7369942196531792, "acc_norm_stderr": 0.02370309952525818 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4346368715083799, "acc_stderr": 0.01657899743549672, "acc_norm": 0.4346368715083799, "acc_norm_stderr": 0.01657899743549672 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7438271604938271, "acc_stderr": 0.0242885336377261, "acc_norm": 0.7438271604938271, "acc_norm_stderr": 0.0242885336377261 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.470013037809648, "acc_stderr": 0.012747248967079069, "acc_norm": 0.470013037809648, "acc_norm_stderr": 0.012747248967079069 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.02824568739146293, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.02824568739146293 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7510204081632653, "acc_stderr": 0.027682979522960234, "acc_norm": 0.7510204081632653, "acc_norm_stderr": 0.027682979522960234 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.038786267710023595, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.038786267710023595 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8538011695906432, "acc_stderr": 0.02709729011807081, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.02709729011807081 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5569155446756426, "mc1_stderr": 0.017389730346877116, "mc2": 0.7113693226923901, "mc2_stderr": 0.014768500069791109 }, "harness\|winogrande\|5": { "acc": 0.8224151539068666, "acc_stderr": 0.010740676861359238 }, "harness\|gsm8k\|5": { "acc": 0.7050796057619408, "acc_stderr": 0.012560698010954767 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
youssef101/artelingo-dummy	--- license: mit dataset_info: features: - name: image dtype: image - name: art_style dtype: string - name: painting dtype: string - name: emotion dtype: string - name: language dtype: string - name: text dtype: string - name: split dtype: string splits: - name: train num_bytes: 18587167692.616 num_examples: 62989 - name: validation num_bytes: 965978050.797 num_examples: 3191 - name: test num_bytes: 2330046601.416 num_examples: 6402 download_size: 4565327615 dataset_size: 21883192344.829002 task_categories: - image-to-text - text-classification - image-classification - text-to-image - text-generation language: - en - ar - zh tags: - Affective Captioning - Emotions - Prediction - Art - ArtELingo pretty_name: ArtELingo size_categories: - 100K<n<1M --- ArtELingo is a benchmark and dataset introduced in a research paper aimed at promoting work on diversity across languages and cultures. It is an extension of ArtEmis, which is a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions. ArtELingo expands this dataset by adding 790,000 annotations in Arabic and Chinese. The purpose of these additional annotations is to evaluate the performance of "cultural-transfer" in AI systems. The dataset in ArtELingo contains many artworks with multiple annotations in three languages, providing a diverse set of data that enables the study of similarities and differences across languages and cultures. The researchers investigate captioning tasks and find that diversity in annotations improves the performance of baseline models. The goal of ArtELingo is to encourage research on multilinguality and culturally-aware AI. By including annotations in multiple languages and considering cultural differences, the dataset aims to build more human-compatible AI that is sensitive to emotional nuances across various cultural contexts. The researchers believe that studying emotions in this way is crucial to understanding a significant aspect of human intelligence. In summary, ArtELingo is a dataset that extends ArtEmis by providing annotations in multiple languages and cultures, facilitating research on diversity in AI systems and improving their performance in emotion-related tasks like label prediction and affective caption generation. The dataset is publicly available, and the researchers hope that it will facilitate future studies in multilingual and culturally-aware artificial intelligence.
kz919/databricks-dolly-15k	--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 14275542 num_examples: 15011 download_size: 8073498 dataset_size: 14275542 license: apache-2.0 language: - en --- # Dataset Card for "databricks-dolly-15k" A instruction tuning formatted version of [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
zh-tw-llm-dv/zh-tw-pythia-ta8000-v1-e1-tr_sg-301-c1024	--- dataset_info: dataset_size: 442704996.0 download_size: 177974029 features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - dtype: string name: preview splits: - name: train num_bytes: 441717119.0 num_examples: 306319 - name: test num_bytes: 987877.0 num_examples: 200 --- # zh-tw-pythia-ta8000-v1-e1-tr_sg-301-c1024 This dataset is a part of the `zh-tw-llm` project. * Tokenizer: `zh-tw-pythia-tokenizer-a8000-v1` * Built with: `translations`, `sharegpt` * Rows: `train` `306319`, `test` `200` * Max length: `1024` * Full config: ```json {"build_with": ["translations", "sharegpt"], "preview_length": 128, "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\nChinese: {lang_2}", "Chinese: {lang_2}\nEnglish: {lang_1}"], "use_template": "random", "rows_limit": 300000, "test_size": 100, "test_split_seed": 42}, "sharegpt_settings": {"source_dataset": "zetavg/ShareGPT-Processed", "train_on_inputs": false, "languages": [{"en": 0.4}, "zh_Hant"], "rows_limit": 8000, "test_size": 0.02, "test_split_seed": 42, "test_rows_limit": 100}} ```
autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574881	--- type: predictions tags: - autotrain - evaluation datasets: - xtreme eval_info: task: entity_extraction model: Cole/xlm-roberta-base-finetuned-panx-de metrics: [] dataset_name: xtreme dataset_config: PAN-X.de dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Cole/xlm-roberta-base-finetuned-panx-de * Dataset: xtreme To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
Codec-SUPERB/musdb18_synth	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 44100 - name: id dtype: string splits: - name: original num_bytes: 15593631670.0 num_examples: 750 - name: academicodec_hifi_16k_320d num_bytes: 5657371720.0 num_examples: 750 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 5657371720.0 num_examples: 750 - name: academicodec_hifi_24k_320d num_bytes: 8485912520.0 num_examples: 750 - name: dac_16k num_bytes: 5657596210.0 num_examples: 750 - name: dac_24k num_bytes: 8486361620.0 num_examples: 750 - name: dac_44k num_bytes: 15593634870.0 num_examples: 750 - name: encodec_24k num_bytes: 8486362626.0 num_examples: 750 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 5657538708.0 num_examples: 750 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 5657538314.0 num_examples: 750 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 5657538314.0 num_examples: 750 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 5657596210.0 num_examples: 750 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 5657596210.0 num_examples: 750 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 5657596210.0 num_examples: 750 - name: speech_tokenizer_16k num_bytes: 5657829320.0 num_examples: 750 download_size: 99080008677 dataset_size: 113221476242.0 configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k path: data/encodec_24k-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* ---
TurtleLiu/Psy_Test	--- license: apache-2.0 ---
Jorgvt/TID2013	--- tags: - image-quality pretty_name: TAMPERE IMAGE DATABASE 2013 size_categories: - 1K<n<10K ---
AI-Golden/aigolden-model	--- license: apache-2.0 ---
Minggz/Vi-Ner	--- task_categories: - token-classification language: - vi tags: - legal size_categories: - 10K<n<100K --- ## Vi-Ner ### Dataset Description ner_tags: a list of classification labels (int). Full tagset with indices: ```python {'B-DATETIME': 0, 'B-LOCATION': 1, 'B-ORGANIZATION': 2, 'B-PERSON': 3, 'I-DATETIME': 4, 'I-LOCATION': 5, 'I-ORGANIZATION': 6, 'I-PERSON': 7, 'O': 8} ``` ### Data Splits \| name \|train\|validation\|test\| \|---------\|----:\|---------:\|---:\| \| Vi-Ner \|19255\| 2407\|2407\| ### Example An example of 'train' looks as follows. ``` { {'tokens': ['NSƯT', 'Hồng', 'Liên', '(trái)', 'đến', 'chúc', 'mừng', 'Thu', 'Trang..'], 'ner_tags': ['B-PERSON', 'I-PERSON', 'I-PERSON', 'O', 'O', 'O', 'O', 'B-PERSON', 'I-PERSON'], 'ner_idx': [3, 7, 7, 8, 8, 8, 8, 3, 7]} } ``` ### Usage ```python import datasets vi_ner = datasets.load_dataset('Minggz/Vi-Ner') vi_ner ```
tj-solergibert/SRV-Europarl-ST-processed-mt-de	--- dataset_info: features: - name: source_text dtype: string - name: dest_text dtype: string - name: dest_lang dtype: string splits: - name: train num_bytes: 142212387.7873306 num_examples: 570077 - name: valid num_bytes: 18480669.707563575 num_examples: 77255 - name: test num_bytes: 18441786.554772235 num_examples: 79827 download_size: 137284138 dataset_size: 179134844.0496664 --- # Dataset Card for "SRV-Europarl-ST-processed-mt-de" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nexdata/2418_Fight_Videos_Data	--- license: cc-by-nc-nd-4.0 --- ## Description 2,418 Fight Videos Data. The data includes indoor scenes (restaurant, arena, bus, boxing room, etc.) and outdoor scenes (road, crosswalk, lawn, etc.). The data covers multiple scenes, different time, multiple weather distribution. The data can be used for tasks such as fight behavior analysis. For more details, please refer to the link: https://www.nexdata.ai/dataset/1061?source=Huggingface # Specifications ## Data size 2,418 videos, including 2,285 fight videos of humans, 5 fight videos of humans and animals, and 128 fight videos of animals ## Collecting environment including indoor scenes (restaurant, arena, bus, boxing room, etc.), outdoor scenes (road, crosswalk, lawn, etc.) ## Diversity multiple scenes, different time, multiple weather distribution ## Divece including camera (very few videos) and cellphone ## Collecting time day, night ## Image Parameter the video data format is .mp4 # Licensing Information Commercial License
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/39661990	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1339 dataset_size: 180 --- # Dataset Card for "39661990" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dputilov/midascontrolourbalanced	--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: mask dtype: image - name: text dtype: string splits: - name: train num_bytes: 1537158001.974634 num_examples: 2725 download_size: 1526594996 dataset_size: 1537158001.974634 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "midascontrolourbalanced" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wenhanhan/PUBHEALTH_train	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 46953437 num_examples: 9804 download_size: 26528789 dataset_size: 46953437 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "PUBHEALTH_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
WelfCrozzo/kupalinka-sum	--- dataset_info: features: - name: x dtype: string - name: y dtype: string - name: lang dtype: string splits: - name: train num_bytes: 839341049 num_examples: 332876 - name: validation num_bytes: 93823377 num_examples: 37057 download_size: 516244839 dataset_size: 933164426 --- # Dataset Card for "kupalinka-sum" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sasha/stablediffusionbias	--- license: cc-by-nc-4.0 ---
TenzinGayche/tts_nocs_b3	--- dataset_info: features: - name: file_name dtype: audio: sampling_rate: 16000 - name: uni dtype: string - name: wylie dtype: string - name: url dtype: string - name: dept dtype: string - name: timestamp dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5624702984.046 num_examples: 92167 download_size: 27280617879 dataset_size: 5624702984.046 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "tts_nocs_b3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
megantron/labeled_aesthetics_simpsons	--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 splits: - name: train num_bytes: 69240975.0 num_examples: 178 download_size: 69245463 dataset_size: 69240975.0 --- # Dataset Card for "labeled_aesthetics_simpsons" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
natnitaract/teetouchjaknamon-faissbatchall-index-2	--- license: cc-by-3.0 ---
irds/wikiclir_de	--- pretty_name: '`wikiclir/de`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `wikiclir/de` The `wikiclir/de` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/de). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=2,091,278 - `queries` (i.e., topics); count=938,217 - `qrels`: (relevance assessments); count=5,550,454 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/wikiclir_de', 'docs') for record in docs: record # {'doc_id': ..., 'title': ..., 'text': ...} queries = load_dataset('irds/wikiclir_de', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/wikiclir_de', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{sasaki-etal-2018-cross, title = "Cross-Lingual Learning-to-Rank with Shared Representations", author = "Sasaki, Shota and Sun, Shuo and Schamoni, Shigehiko and Duh, Kevin and Inui, Kentaro", booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N18-2073", doi = "10.18653/v1/N18-2073", pages = "458--463" } ```
joey234/mmlu-high_school_mathematics-rule-neg	--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 55920 num_examples: 270 download_size: 33612 dataset_size: 55920 --- # Dataset Card for "mmlu-high_school_mathematics-rule-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AdapterOcean/med_alpaca_standardized_cluster_98_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 13488443 num_examples: 7117 download_size: 7232687 dataset_size: 13488443 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_98_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dmargutierrez/Babelscape-wikineural-joined	--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: int64 - name: lang dtype: string splits: - name: train num_bytes: 319328641 num_examples: 821600 - name: validation num_bytes: 39434957 num_examples: 102700 - name: test num_bytes: 39371980 num_examples: 103206 download_size: 139847318 dataset_size: 398135578 task_categories: - token-classification language: - es - en - nl - fr - it - ru - pt - pl - de pretty_name: Wikineural tags: - named-entity-recognition - wikipedia - machine-generation --- # Dataset Card for "Babelscape-wikineural-joined" This dataset is a merged version of [wikineural](https://huggingface.co/datasets/Babelscape/wikineural) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) <pre><code> @inproceedings{tedeschi-etal-2021-wikineural-combined, title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", author = "Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\`o} and Cecconi, Francesco and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.215", pages = "2521--2533", abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", } </code></pre>
gigant/tib_2303_filtered	--- dataset_info: features: - name: doi dtype: string - name: title dtype: string - name: url dtype: string - name: video_url dtype: string - name: license dtype: string - name: subject dtype: string - name: genre dtype: string - name: release_year dtype: string - name: author dtype: string - name: contributors dtype: string - name: abstract dtype: string - name: transcript dtype: string - name: transcript_segments sequence: - name: id dtype: int32 - name: seek dtype: int32 - name: start dtype: float32 - name: end dtype: float32 - name: text dtype: string - name: tokens sequence: int32 - name: temperature dtype: float32 - name: avg_logprob dtype: float32 - name: compression_ratio dtype: float32 - name: no_speech_prob dtype: float32 - name: keyframes sequence: - name: slide dtype: string - name: frames sequence: int32 - name: timestamp sequence: float32 - name: language dtype: string splits: - name: train num_bytes: 1032202393.542393 num_examples: 9103 download_size: 500663533 dataset_size: 1032202393.542393 --- # Dataset Card for "tib_2303_filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sloango/github-issues	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: labels list: - name: id dtype: int64 - name: node_id dtype: string - name: url dtype: string - name: name dtype: string - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: assignees list: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: comments sequence: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: body dtype: string - name: reactions struct: - name: url dtype: string - name: total_count dtype: int64 - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: laugh dtype: int64 - name: hooray dtype: int64 - name: confused dtype: int64 - name: heart dtype: int64 - name: rocket dtype: int64 - name: eyes dtype: int64 - name: timeline_url dtype: string - name: state_reason dtype: string - name: draft dtype: bool - name: pull_request struct: - name: url dtype: string - name: html_url dtype: string - name: diff_url dtype: string - name: patch_url dtype: string - name: merged_at dtype: timestamp[s] - name: is_pull_request dtype: bool splits: - name: train num_bytes: 30030287 num_examples: 5000 download_size: 8781855 dataset_size: 30030287 --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mvkvc/artifact-10k	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ai '1': real splits: - name: train num_bytes: 99148840.0 num_examples: 8000 - name: test num_bytes: 25474768.0 num_examples: 2000 download_size: 125994503 dataset_size: 124623608.0 --- # Dataset Card for "critique-10k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MaxYuki/Izuku_Midoriya	--- license: apache-2.0 ---
Back-up/chung-khoan-demo-p7	--- dataset_info: features: - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: view struct: - name: number_of_response dtype: string - name: number_of_view dtype: string - name: content list: - name: res dtype: string splits: - name: train num_bytes: 44370120 num_examples: 8859 download_size: 15521580 dataset_size: 44370120 configs: - config_name: default data_files: - split: train path: data/train-* ---
Luciya/llama-2-nuv-intent-noE-oos	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 828135 num_examples: 1834 download_size: 127293 dataset_size: 828135 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "llama-2-nuv-intent-noE-oos" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zooxsmartufpb/dataset_complete3	--- dataset_info: features: - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 81060969 num_examples: 46099 download_size: 8042824 dataset_size: 81060969 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dataset_complete3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
PleIAs/pd_authors_openlibrary	Invalid username or password.
NebulaByte/E-Commerce_FAQs	--- license: apache-2.0 ---
Deysi/spam-detection-dataset	--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 3161821 num_examples: 8175 - name: test num_bytes: 1094757 num_examples: 2725 download_size: 2578551 dataset_size: 4256578 license: apache-2.0 task_categories: - text-classification language: - en pretty_name: spam size_categories: - 10K<n<100K --- # Dataset Card for "spam-detection-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hpprc/jagovfaqs	--- dataset_info: - config_name: collection features: - name: text dtype: string splits: - name: train num_bytes: 13918890 num_examples: 22794 download_size: 5874592 dataset_size: 13918890 - config_name: dataset features: - name: anc dtype: string - name: pos_ids sequence: int64 - name: neg_ids sequence: 'null' splits: - name: train num_bytes: 4469001 num_examples: 22794 download_size: 1924819 dataset_size: 4469001 configs: - config_name: collection data_files: - split: train path: collection/train-* - config_name: dataset data_files: - split: train path: dataset/train-* ---
TrainingDataPro/monitors-replay-attacks-dataset	--- license: cc-by-nc-nd-4.0 task_categories: - video-classification language: - en tags: - legal dataset_info: features: - name: file dtype: string - name: phone dtype: string - name: computer dtype: string - name: gender dtype: string - name: age dtype: int16 - name: country dtype: string splits: - name: train num_bytes: 588 num_examples: 10 download_size: 342902185 dataset_size: 588 --- # Monitors Replay Attacks Dataset The dataset consists of videos of replay attacks played on different models of computers. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems. The dataset includes: replay attacks - videos of real people played on a computer and filmed on the phone. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2Fa40451e66953bd1652887400c0eae4be%2FUntitled.png?generation=1688049829507934&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/anti-spoofing-replay?utm_source=huggingface&utm_medium=cpc&utm_campaign=monitors-replay-attacks-dataset) to discuss your requirements, learn about the price and buy the dataset. # Content The folder "attacks" includes videos of replay attacks ### Computer companies in the datset: - Dell - LG - ASUS - HP - Redmi - AOC - Samsung ### File with the extension .csv includes the following information for each media file: - file: link to access the replay video, - phone: the device used to capture the replay video, - computer: the device used to play the video, - gender: gender of a person in the video, - age: age of the person in the video, - country: country of the person ## [TrainingData](https://trainingdata.pro/data-market/anti-spoofing-replay?utm_source=huggingface&utm_medium=cpc&utm_campaign=monitors-replay-attacks-dataset) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
mshenoda/spam-messages	--- license: mit --- ## Dataset The dataset is composed of messages labeled by ham or spam, merged from three data sources: - SMS Spam Collection https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset - Telegram Spam Ham https://huggingface.co/datasets/thehamkercat/telegram-spam-ham/tree/main - Enron Spam: https://huggingface.co/datasets/SetFit/enron_spam/tree/main (only used message column and labels) The prepare script for enron is available at https://github.com/mshenoda/roberta-spam/tree/main/data/enron. The data is split 80% train 10% validation, and 10% test sets; the scripts used to split and merge of the three data sources are available at: https://github.com/mshenoda/roberta-spam/tree/main/data/utils. ### Dataset Class Distribution Training 80% \| Validation 10% \| Testing 10% :-------------------------:\|:-------------------------:\|:-------------------------: ![](plots/train_set_distribution.jpg "Train Distribution") Class Distribution \| ![](plots/val_set_distribution.jpg "Validation Distribution") Class Distribution \| ![](plots/test_set_distribution.jpg "Test Distribution") Class Distribution
Andregomes/nlpC	--- license: bigscience-openrail-m ---
3mrys/daset_2	--- license: apache-2.0 ---
dalow24/testing	--- license: afl-3.0 ---
stevied67/autotrain-data-pegasus-reddit-summarizer	--- language: - en task_categories: - summarization --- # AutoTrain Dataset for project: pegasus-reddit-summarizer ## Dataset Description This dataset has been automatically processed by AutoTrain for project pegasus-reddit-summarizer. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_id": "82n2za", "text": "User who has been working in sales for 30+ years gets a new laptop on Monday. This morning when I get in, my phone is ringing already. I'm not supposed to start for another 20 mins, but I'm nice, so I answer it.\n\n\"This new laptop doesn't have Microsoft on it. Do I need to bring it back in? Just I'm in Scotland, so I'll have to fly down again.\"\n\nEr, yes it does. We went through it when I handed it over, I showed you Outlook, and how Outlook 2016 looks ever so slightly different to Outlook 2010 on your old laptop.\n\n\"Look, it's not there. Every time I click on the button, it just opens the internet. I've emailed my boss from my phone to let him know I'm cancelling all my appointments today, so can you fix it over the VPN or do I need to fly down?\"\n\nSo, I ask him what he's clicking on. \"The blue E. You said the icon was blue now instead of orange. But that just opens the internet, I've already TOLD YOU.\"\n\nI ask him to look along the taskbar for any other blue icons. \"There's a blue and white O. Are you telling me that's it?\" I ask him to confirm that Outlook begins with the letter O, and advise him to try clicking on that icon instead.\n\nSo he clicks on it, and ta-da! Outlook opens. \"Oh for God's sake. This is too confusing. Why did you change the colour anyway? Now I have to re-arrange all my appointments, this is really inconvenient.\"\n\nSorry, I did ring up my mate Bill and ask him to change the colour of Outlook from orange to blue just to confuse you. Luckily I have great power and influence over at Microsoft, so they did me a favour, and I'm now reaping the untold rewards.\n\nGTG, writing an email to his boss to cover my arse...\n", "target": "User receives a new laptop and complains to IT that it doesn't have Microsoft on it. IT informs the user that they had gone through it when handing it over and that the user had simply clicked on the wrong icon. The user complains about the change in icon color and that they now have to rearrange their entire schedule. IT sarcastically apologizes and writes an email to cover themselves." }, { "feat_id": "q4kjoe", "text": "The title implies I was there but really it was just my mom and my sister.\n\nMy sister was craving a cheddar jalapeo bagel so my mom decided to go to a chain caf to get one for her. It was 10 minutes before closing, and they went through the drive thru. My mom orders the cheddar bagel for my sister plus some other things for the rest of the people at home, including coffee cake. The gal at the drive thru window said \"you're lucky, you're getting the last ones of everything you're ordering!\"\n\nMy mom pulls up to the window to pay and receive the food and the drive thru gal (about 19) is crying and apologizing profusely. She says the people in front of my mom STOLE THE FOOD. Mom asked how it happened and the lady said that she had made a mistake and was about to give the car in front the wrong order, but she realized her mistake before handing it over and announced it. The people then REACHED for the bag (it was not handed to them!!!) and stole it, apparently saying \"you can't have it back now, it's cross contaminated!\" Then when the lady called for her manager, he was busy, and the people's order wasn't ready yet, so the poor gal just told them to pull up and wait for their food and they did.\n\nMy mom is a really loving person and so she's trying to tell this lady it's okay, she didn't really need the food, she's not mad, etc., and in the meantime the manager comes over to ask what is happening. She tells him and he is shocked. He asked if the car in front was those people, and she said yes. So he starts going out to talk to the people in the car, and at that moment, they step on it and zip out of the parking lot. \n\nSo now those people have not only stolen my mom's order, which were the last items, but they didn't even receive their order! But the good news is that the manager said to my mom that he had been saving a cheddar bagel for himself and that he would give that one to her free of charge. \n\nHave you ever heard of anything like this??? My mom told me this on the phone and I was stunned. I've worked food service before but nothing like this has ever happened!! She thinks the people in the other car had done this maneuver before since the \"cross contamination\" response came out way too quickly. Also I feel so sorry for the lady! She's working in a fucking pandemic getting underpaid and overworked and now has to deal with deranged people!", "target": "A woman went to a chain caf\u00e9 with her daughter to buy a cheddar jalape\u00f1o bagel for her sister. The drive thru attendant announces they are getting the last items of everything. The attendant then reveals that the people in the car in front of them stole their food. The woman's mother attempted to comfort the attendant and the manager offered the woman a cheddar bagel for free. The woman wonders if the \"cross contamination\" defense may have been used by the thieves before." } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_id": "Value(dtype='string', id=None)", "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 7200 \| \| valid \| 1800 \|
tyzhu/squad_qa_wrong_num_v5_full_recite_full_passage_random_permute_rerun_1	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 5171543.277877698 num_examples: 2875 - name: validation num_bytes: 584108 num_examples: 300 download_size: 1519861 dataset_size: 5755651.277877698 --- # Dataset Card for "squad_qa_wrong_num_v5_full_recite_full_passage_random_permute_rerun_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
UnderstandLing/oasst1_bn	--- license: apache-2.0 dataset_info: features: - name: message_id dtype: string - name: parent_id dtype: string - name: user_id dtype: string - name: created_date dtype: string - name: text dtype: string - name: role dtype: string - name: lang dtype: string - name: review_count dtype: int64 - name: review_result dtype: bool - name: deleted dtype: bool - name: rank dtype: float64 - name: synthetic dtype: bool - name: model_name dtype: 'null' - name: detoxify struct: - name: identity_attack dtype: float64 - name: insult dtype: float64 - name: obscene dtype: float64 - name: severe_toxicity dtype: float64 - name: sexual_explicit dtype: float64 - name: threat dtype: float64 - name: toxicity dtype: float64 - name: message_tree_id dtype: string - name: tree_state dtype: string - name: emojis struct: - name: count sequence: int64 - name: name sequence: string - name: labels struct: - name: count sequence: int64 - name: name sequence: string - name: value sequence: float64 splits: - name: train num_bytes: 117761745 num_examples: 83078 - name: validation num_bytes: 5686930 num_examples: 3946 download_size: 31092337 dataset_size: 123448675 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
dwadden/healthver_entailment	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking pretty_name: HealthVer dataset_info: features: - name: claim_id dtype: int32 - name: claim dtype: string - name: abstract_id dtype: int32 - name: title dtype: string - name: abstract sequence: string - name: verdict dtype: string - name: evidence sequence: int32 splits: - name: train num_bytes: 9490482 num_examples: 5292 - name: validation num_bytes: 1707997 num_examples: 940 - name: test num_bytes: 1620257 num_examples: 903 download_size: 3610222 dataset_size: 12818736 --- # Dataset Card for "healthver_entailment" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) ## Dataset Description - Repository: <https://github.com/sarrouti/HealthVe> - Point of Contact: [David Wadden](mailto:davidw@allenai.org) ### Dataset Summary HealthVer is a dataset of public health claims, verified against scientific research articles. For this version of the dataset, we follow the preprocessing from the MultiVerS modeling paper https://github.com/dwadden/multivers, verifying claims against full article abstracts rather than individual sentences. Entailment labels and rationales are included. ## Dataset Structure ### Data fields - `claim_id`: An `int32` claim identifier. - `claim`: A `string`. - `abstract_id`: An `int32` abstract identifier. - `title`: A `string`. - `abstract`: A list of `strings`, one for each sentence in the abstract. - `verdict`: The fact-checking verdict, a `string`. - `evidence`: A list of sentences from the abstract which provide evidence for the verdict.
lingtrain/sanskrit-russian	--- dataset_info: features: - name: ru dtype: string - name: san dtype: string splits: - name: train num_bytes: 13189111 num_examples: 32669 download_size: 7022744 dataset_size: 13189111 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sanskrit-russian" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
heliosprime/twitter_dataset_1712952491	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 14426 num_examples: 33 download_size: 10088 dataset_size: 14426 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1712952491" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kevmo314/YouTube-Speech-Dataset

--- license: other ---

open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M

--- pretty_name: Evaluation run of cerebras/Cerebras-GPT-111M dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cerebras/Cerebras-GPT-111M](https://huggingface.co/cerebras/Cerebras-GPT-111M)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T19:15:45.776483](https://huggingface.co/datasets/open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M/blob/main/results_2023-09-22T19-15-45.776483.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\": 0.0010486577181208054,\n\ \ \"em_stderr\": 0.00033145814652193176,\n \"f1\": 0.021427223154362497,\n\ \ \"f1_stderr\": 0.0008720566428263053,\n \"acc\": 0.23875295974743488,\n\ \ \"acc_stderr\": 0.00701912891202994\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652193176,\n\ \ \"f1\": 0.021427223154362497,\n \"f1_stderr\": 0.0008720566428263053\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.47750591949486976,\n\ \ \"acc_stderr\": 0.01403825782405988\n }\n}\n```" repo_url: https://huggingface.co/cerebras/Cerebras-GPT-111M 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_19T13_47_12.878137 path: - '**/details_harness|arc:challenge|25_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T13:47:12.878137.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_22T19_15_45.776483 path: - '**/details_harness|drop|3_2023-09-22T19-15-45.776483.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-22T19-15-45.776483.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T19_15_45.776483 path: - '**/details_harness|gsm8k|5_2023-09-22T19-15-45.776483.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-22T19-15-45.776483.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hellaswag|10_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:47:12.878137.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:47:12.878137.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_47_12.878137 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T13:47:12.878137.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T13:47:12.878137.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T19_15_45.776483 path: - '**/details_harness|winogrande|5_2023-09-22T19-15-45.776483.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-22T19-15-45.776483.parquet' - config_name: results data_files: - split: 2023_07_19T13_47_12.878137 path: - results_2023-07-19T13:47:12.878137.parquet - split: 2023_09_22T19_15_45.776483 path: - results_2023-09-22T19-15-45.776483.parquet - split: latest path: - results_2023-09-22T19-15-45.776483.parquet --- # Dataset Card for Evaluation run of cerebras/Cerebras-GPT-111M ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/cerebras/Cerebras-GPT-111M - **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 [cerebras/Cerebras-GPT-111M](https://huggingface.co/cerebras/Cerebras-GPT-111M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T19:15:45.776483](https://huggingface.co/datasets/open-llm-leaderboard/details_cerebras__Cerebras-GPT-111M/blob/main/results_2023-09-22T19-15-45.776483.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": 0.0010486577181208054, "em_stderr": 0.00033145814652193176, "f1": 0.021427223154362497, "f1_stderr": 0.0008720566428263053, "acc": 0.23875295974743488, "acc_stderr": 0.00701912891202994 }, "harness|drop|3": { "em": 0.0010486577181208054, "em_stderr": 0.00033145814652193176, "f1": 0.021427223154362497, "f1_stderr": 0.0008720566428263053 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.47750591949486976, "acc_stderr": 0.01403825782405988 } } ``` ### 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]

Nexdata/Human_Pose_Recognition_Data

--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Human_Pose_Recognition_Data ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.nexdata.ai/datasets/1132?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary 10,000 People - Human Pose Recognition Data. This dataset includes indoor and outdoor scenes.This dataset covers males and females. Age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The data diversity includes different shooting heights, different ages, different light conditions, different collecting environment, clothes in different seasons, multiple human poses. For each subject, the labels of gender, race, age, collecting environment and clothes were annotated. The data can be used for human pose recognition and other tasks. For more details, please refer to the link: https://www.nexdata.ai/datasets/1132?source=Huggingface ### Supported Tasks and Leaderboards object-detection, computer-vision: The dataset can be used to train a model for object detection. ### 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 Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions

Chaymaa/grdf-inference-aug-iter2

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 38284884.0 num_examples: 456 - name: valid num_bytes: 10478667.684210526 num_examples: 113 - name: test num_bytes: 118581.31578947368 num_examples: 1 download_size: 46290889 dataset_size: 48882133.0 --- # Dataset Card for "grdf-inference-aug-iter2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Graphcore/wikipedia-bert-128

--- language: - en license: - cc-by-sa-3.0 ---

brainer/korean-medicine-prescription

--- dataset_info: features: - name: STND_Y dtype: int64 - name: IDV_ID dtype: int64 - name: KEY_SEQ dtype: int64 - name: SEQ_NO dtype: int64 - name: SEX dtype: int64 - name: AGE_GROUP dtype: int64 - name: SIDO dtype: int64 - name: RECU_FR_DT dtype: string - name: GNL_NM_CD dtype: string - name: DD_MQTY_FREQ dtype: float64 - name: DD_EXEC_FREQ dtype: int64 - name: MDCN_EXEC_FREQ dtype: int64 - name: UN_COST dtype: float64 - name: AMT dtype: int64 - name: DATA_STD_DT dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4651797950 num_examples: 32053871 download_size: 903450347 dataset_size: 4651797950 configs: - config_name: default data_files: - split: train path: data/train-* --- ```json { 'STND_Y': '기준년도', 'IDV_ID': '가입자 일련번호', 'KEY_SEQ': '진료내역 일련번호진료내역 일련번호', 'SEQ_NO': '일련번호', 'SEX': '성별코드', 'AGE_GROUP': '연령대 코드', 'SIDO': '시도코드', 'RECU_FR_DT': '요양개시일자', 'GNL_NM_CD': '약품 일반성분명 코드', 'DD_MQTY_FREQ': '1회투약량', 'DD_EXEC_FREQ': '1일투약량', 'MDCN_EXEC_FREQ': '총투여일수또는실시횟수', 'UN_COST': '단가', 'AMT': '금액', 'DATA_STD_DT': '데이터 기준일자' } ``` [데이터 출처](https://www.data.go.kr/data/15007117/fileData.do) [출처1](http://www.seoulhealth.kr/down/pdfView?atchId=595&pdfId=595&isMobile=N), [출처2](https://www.hira.or.kr/cms/participation/05/07/__icsFiles/afieldfile/2014/05/16/4_1.pdf), [출처3](http://www.khmsri.or.kr/common/file_download.jsp?filePath=/ewmri/upload/board/ewmri_seminar/upfile/&fileName=%EC%B2%AD%EA%B5%AC%EB%8D%B0%EC%9D%B4%ED%84%B0%EC%86%8C%EA%B0%9C_%EA%B9%80%EC%A7%80%EC%95%A0_%EA%B2%BD%ED%9D%AC%EB%8C%80.pdf) | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | | --- | --- | --- | --- | --- | | 1 | 기준년도 | STND_Y | | 해당 정보의 기준년도를 제공함 | | 2 | 가입자 일련번호 | IDV_ID | | 해당가입자에 부여한 일련번호 (1 ~ 1,000,000) | | 3 | 진료내역 일련번호 | KEY_SEQ | | 해당진료내역에 대한 일련번호 | | 4 | 일련번호 | SEQ_NO | | 해당 약품 일련번호 | | 5 | 성별코드 | SEX | | 해당 정보 대상자의 성별을 제공함 (성별: 1(남자), 2(여자)) | | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | 그룹 | 연령대 | 그룹 | 연령대 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 5 | 연령대 코드 | AGE_GROUP | | 기준년도에 수진자의 나이를 5세 단위로 그룹화하여 구분한 코드 (총 18개 그룹) | 1 | 0~4세 | 10 | 45~49세 | | | | | | 0~84세까지 5세 단위 그룹화, 85세 이상은 85+로 그룹화 | 2 | 5~9세 | 11 | 50~54세 | | | | | | | 3 | 10~14세 | 12 | 55~59세 | | | | | | | 4 | 15~19세 | 13 | 60~64세 | | | | | | | 5 | 20~24세 | 14 | 65~69세 | | | | | | | 6 | 25~29세 | 15 | 70~74세 | | | | | | | 7 | 30~34세 | 16 | 75~79세 | | | | | | | 8 | 35~39세 | 17 | 80~84세 | | | | | | | 9 | 40~44세 | 18 | 85세+ | | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | 코드명 | 시도명 | 코드명 | 시도명 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 6 | 시도코드 | SIDO | | 해당 수진자 거주지의 시도코드 (2012년부터 세종특별자치시가 신규로 편입됨에 따라, 2011년까지의 데이터에는 해당 항목이 존재하지 않음) | 11 | 서울특별시 | 42 | 강원도 | | | | | | | 26 | 부산광역시 | 43 | 충청북도 | | | | | | | 27 | 대구광역시 | 44 | 충청남도 | | | | | | | 28 | 인천광역시 | 45 | 전라북도 | | | | | | | 29 | 광주광역시 | 46 | 전라남도 | | | | | | | 30 | 대전광역시 | 47 | 경상북도 | | | | | | | 31 | 울산광역시 | 48 | 경상남도 | | | | | | | 36 | 세종특별자치시 | 49 | 제주특별자치도 | | | | | | | 41 | 경기도 | | | | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | 구분 | 의과_보건기관 | | --- | --- | --- | --- | --- | --- | --- | | 8 | 서식코드 | FORM_CD | | 명세서 서식구분을 위한 코드; 의과_보건기관에서 진료한 환자의 진료형태를 구분함 | 02 | 의과 입원 | | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | 구분 | 의과_보건기관 | | --- | --- | --- | --- | --- | --- | --- | | 9 | 진료과목 코드 | DSBJT_CD | | 의과 26종의 진료과목코드에 따라 병원급 이상의 진료기관일 경우 실제진료를 받은 진료과목, 의원급 의료기관일 경우 상병 명에 해당되는 진료과목 | 0 | 일반의 | | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | | --- | --- | --- | --- | --- | | 10 | 주상병 코드 | MAIN_SICK | | 명세서 상의 주상병의 분류기호 - 통계청 고시에 따른「한국표준 질병·사인 분류 4, 5, 6차 상병 분류 기호 참조 | | 11 | 부상병 코드 | SUB_SICK | | 명세서 상의 주된 상병분류기호 외의 추가 상병(부상병)의 분류 기호 - 결측(ZZ), 정상 또는 해당사항 없음(-)으로 표시 - 통계청 고시에 따른「한국표준 질병·사인 분류 4, 5, 6차 상병 분류 기호 참조 | | 12 | 요양일수 | VSCN | | 수진자가 요양급여를 받은 실 일수 - 입원 또는 내원일수에 원내 투약일수를 산입하여 기재 - 내원일수는 초진과 재진을 포함함 | | 13 | 입내원 일수 | RECN | | (입원진료) 수진자가 진료를 받기 위해 요양기관에 입원한 날부터 퇴원 일까지의 실 일수 - (내원진료) 수진자가 내원하여 진료를 받은 실 일 수 | | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | 요양기관 종류 | 건강보험 | 의료급여 | | --- | --- | --- | --- | --- | --- | --- | --- | | 14 | 심결가산율 | EDEC_ADD_RT | | 요양개시일자 기준으로 종별 규모에 따라 시설, 인력, 장비 등의 투자비용 등을 고려하여 요양기관 종별에 따라 가산 적용되는 진료비의 가산율(%) | 상급 종합병원 | 30% | 22% | | | | | | | 종합병원 | 25% | 18% | | | | | | | 병원(요양병원 포함) | 20% | 15% | | | | | | | 의원, 보건의료원 등 | 15% | 11% | | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | | --- | --- | --- | --- | --- | | 15 | 심결요양 급여비용 총액 | EDEC_TRAMT | | 심결요양급여비용총액은 정산심사결과 수진자 본인이 부담해야 될 금액인「심결본인부담금」과 보험자가 부담해야 할 「심결 보험자부담금」합친 금액 | | 16 | 심결본인 부담금 | EDEC_SBRDN_AMT | | 요양급여비용심사결과를 통해 결정된 수진자 본인이 부담해야 할 부담금 - 국민건강보험법 시행령 [별표2]에 따른 본인일부부담금(동법 시행령 별표2 제4호 및 제5호에 따른 금액을 제외)에서 10원 미만 절사한 금액 | | 17 | 심결 보험자 부담금 | EDEC_JBRDN_AMT | | 심결요양급여비용총액에서 본인일부부담금을 제외한 금액으로 보험자가 부담하여야 하는 금액 | | 18 | 총처방 일수 | TOT_PRES_DD_CNT | | 처방전을 발급한 경우에 해당 처방전에 따라 조제 투약하도록 처방한 일수의 합 | | 19 | 데이터 기준일자 | DATA_STD_DT | | 데이터 작성 기준 일자 | | 연번 | 제공항목 | 표준항목명 | 영문명 | 설명 | | --- | --- | --- | --- | --- | | | 단가 | UN_COST | | 처방 내역상의 의약품 단가 제품코드 약품명 제약사명 규격단위 상한금액 실구입가 단가 641100180 A연질 캅셀 A제약 1캅셀 245 247 245 | | | 금액 | AMT | | 단가, 1회 투약량, 1일 투약량, 총투여일수를 곱한 금액 예시) 652101370 트렌탈 400 서방정 1회 1정, 1일 2회, 2일 투여시 ⥤ 190(원)×1(정)×2(회)×2(일)= 760원 ※ 단, 규격에 따라서 금액이 상이하게 나올수 있으니 매 월 고시되는 보건복지부 고시「약제급여목록 및 급여 상한금액표」참고하여야 함 |

iambestfeed/corpus_gen

--- dataset_info: features: - name: text dtype: string - name: level dtype: string - name: topic dtype: string splits: - name: train num_bytes: 301290168 num_examples: 109990 download_size: 121315554 dataset_size: 301290168 configs: - config_name: default data_files: - split: train path: data/train-* ---

yashnbx/l27b-E02-large-b05-0584-3

--- dataset_info: features: - name: id dtype: int64 - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: test num_bytes: 1011775 num_examples: 146 - name: train num_bytes: 4032267 num_examples: 584 download_size: 831330 dataset_size: 5044042 --- # Dataset Card for "l27b-E02-large-b05-0584-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/tartaglia_genshin

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tartaglia_genshin This is the dataset of tartaglia_genshin, containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 419 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 419 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 419 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 419 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

bigcode/commits-pjj-2048

--- dataset_info: features: - name: commit dtype: string - name: subject dtype: string - name: old_file dtype: string - name: new_file dtype: string - name: new_contents dtype: string - name: old_contents dtype: string - name: lang dtype: string - name: proba dtype: float64 splits: - name: train num_bytes: 49066548725.366234 num_examples: 1847311 download_size: 3264623361 dataset_size: 49066548725.366234 --- # Dataset Card for "commits-pjj-2048" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-131000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1094022 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

brantfetter/primary

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

arthurmluz/xlsum_data-wiki_cstnews_1024_results

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 29742589 num_examples: 7175 download_size: 18278365 dataset_size: 29742589 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "xlsum_data-wiki_cstnews_1024_results" rouge={'rouge1': 0.21341914887481395, 'rouge2': 0.055688000356489714, 'rougeL': 0.13199232049785112, 'rougeLsum': 0.13199232049785112} Bert={'precision': 0.6575144631688188, 'recall': 0.7245010691569658, 'f1': 0.6890005766888528} mover = 0.568884719739508

waveww/guanaco-llama2-1k

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966693 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HeshamMamdouh/dataset-LANS-processesed

--- dataset_info: features: - name: address dtype: string - name: title dtype: string - name: paragraph sequence: string - name: summary dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 2708388021 num_examples: 282281 download_size: 356101098 dataset_size: 2708388021 --- # Dataset Card for "dataset-LANS-processesed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pphuc25/gsm8k_500_new

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 270557.99848369975 num_examples: 500 download_size: 157994 dataset_size: 270557.99848369975 configs: - config_name: default data_files: - split: test path: data/test-* ---

ChunB1/books_adv

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 109344916 num_examples: 960000 download_size: 61970737 dataset_size: 109344916 configs: - config_name: default data_files: - split: train path: data/train-* ---

davanstrien/embedded-cards

--- dataset_info: features: - name: id dtype: string - name: author dtype: string - name: last_modified dtype: timestamp[us, tz=UTC] - name: downloads dtype: int64 - name: likes dtype: int64 - name: paperswithcode_id dtype: string - name: tags sequence: string - name: lastModified dtype: timestamp[us, tz=UTC] - name: createdAt dtype: string - name: key dtype: string - name: created dtype: timestamp[us] - name: card dtype: string - name: embedding sequence: float32 - name: library_name dtype: string - name: pipeline_tag dtype: string - name: mask_token dtype: 'null' - name: card_data dtype: 'null' - name: widget_data dtype: 'null' - name: model_index dtype: 'null' - name: config dtype: 'null' - name: transformers_info dtype: 'null' - name: spaces dtype: 'null' - name: safetensors dtype: 'null' - name: transformersInfo dtype: 'null' - name: modelId dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 472972552 num_examples: 85143 download_size: 309488689 dataset_size: 472972552 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/neon_nikke

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of neon/ネオン/尼恩/네온 (Nikke: Goddess of Victory) This is the dataset of neon/ネオン/尼恩/네온 (Nikke: Goddess of Victory), containing 141 images and their tags. The core tags of this character are `glasses, green_eyes, hat, breasts, long_hair, white_headwear, beret, bangs, white_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 141 | 191.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 141 | 109.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 304 | 217.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 141 | 167.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 304 | 302.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neon_nikke/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/neon_nikke', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1boy, 1girl, hetero, solo_focus, navel, nipples, penis, pussy, thighhighs, blush, uncensored, vaginal, yellow_eyes, open_mouth, medium_breasts, sex_from_behind, nude, cross-section, from_below, sailor_collar, testicles | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blue_neckerchief, bottomless, looking_at_viewer, medium_breasts, navel, pussy, solo, spread_legs, anus, ass, leg_up, no_bra, sailor_collar, serafuku, underboob, white_thighhighs, yellow_eyes, open_mouth, small_breasts, uncensored, from_below, nipple_slip, rock, shirt | | 2 | 18 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, solo, looking_at_viewer, pleated_skirt, white_thighhighs, short_sleeves, white_skirt, sailor_collar, open_mouth, ass, hair_ornament, one-piece_swimsuit, rifle, brown_gloves, grey_hair, looking_back, smile, swimsuit_under_clothes, thigh_boots, white_shirt, high_heels, holding, school_uniform, white_footwear | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, ass, looking_at_viewer, skindentation, solo, thighs, white_shirt, open_mouth, white_thighhighs, outdoors, short_sleeves, bag, sailor_hat, crop_top, from_behind, looking_back, pleated_skirt, serafuku, medium_breasts, red-framed_eyewear, short_hair, belt_pouch, high_heels, neckerchief, panties, under-rim_eyewear, white_footwear, :d, building, midriff, miniskirt, sailor_shirt, shoes, white_skirt | | 4 | 19 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | looking_at_viewer, open_mouth, 1girl, large_breasts, sailor_collar, grey_hair, hairband, navel, white_bikini, cleavage, outdoors, pink_skirt, red-framed_eyewear, solo, blush, wet, choker, day, hair_bow, innertube, bikini_skirt, front-tie_top, holding, side-tie_bikini_bottom, sky, cloud, medium_breasts, :d, beach, pink_bikini, water | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | solo_focus | navel | nipples | penis | pussy | thighhighs | blush | uncensored | vaginal | yellow_eyes | open_mouth | medium_breasts | sex_from_behind | nude | cross-section | from_below | sailor_collar | testicles | blue_neckerchief | bottomless | looking_at_viewer | solo | spread_legs | anus | ass | leg_up | no_bra | serafuku | underboob | white_thighhighs | small_breasts | nipple_slip | rock | shirt | pleated_skirt | short_sleeves | white_skirt | hair_ornament | one-piece_swimsuit | rifle | brown_gloves | grey_hair | looking_back | smile | swimsuit_under_clothes | thigh_boots | white_shirt | high_heels | holding | school_uniform | white_footwear | skindentation | thighs | outdoors | bag | sailor_hat | crop_top | from_behind | red-framed_eyewear | short_hair | belt_pouch | neckerchief | panties | under-rim_eyewear | :d | building | midriff | miniskirt | sailor_shirt | shoes | large_breasts | hairband | white_bikini | cleavage | pink_skirt | wet | choker | day | hair_bow | innertube | bikini_skirt | front-tie_top | side-tie_bikini_bottom | sky | cloud | beach | pink_bikini | water | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:-------------|:--------|:----------|:--------|:--------|:-------------|:--------|:-------------|:----------|:--------------|:-------------|:-----------------|:------------------|:-------|:----------------|:-------------|:----------------|:------------|:-------------------|:-------------|:--------------------|:-------|:--------------|:-------|:------|:---------|:---------|:-----------|:------------|:-------------------|:----------------|:--------------|:-------|:--------|:----------------|:----------------|:--------------|:----------------|:---------------------|:--------|:---------------|:------------|:---------------|:--------|:-------------------------|:--------------|:--------------|:-------------|:----------|:-----------------|:-----------------|:----------------|:---------|:-----------|:------|:-------------|:-----------|:--------------|:---------------------|:-------------|:-------------|:--------------|:----------|:--------------------|:-----|:-----------|:----------|:------------|:---------------|:--------|:----------------|:-----------|:---------------|:-----------|:-------------|:------|:---------|:------|:-----------|:------------|:---------------|:----------------|:-------------------------|:------|:--------|:--------|:--------------|:--------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | | X | | | X | | | X | | | X | | X | X | X | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 18 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | | X | | | | | | | | | | | | X | | | | | | X | | | | X | X | | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | | X | | | | | | | | | | | | X | X | | | | | | | | | X | X | | | X | | | X | | X | | | | | X | X | X | | | | | | X | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 4 | 19 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | | X | | | X | | | | | X | | | | X | X | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | X | | | | | X | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

AdapterOcean/code_instructions_standardized_cluster_18_std

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 13708885 num_examples: 18198 download_size: 6325033 dataset_size: 13708885 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_18_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/okunoda_miyoi_touhou

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of okunoda_miyoi (Touhou) This is the dataset of okunoda_miyoi (Touhou), containing 242 images and their tags. The core tags of this character are `pink_hair, blue_headwear, hat, green_eyes, breasts, short_hair, bangs, hair_between_eyes, animal_hat, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 242 | 313.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 242 | 178.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 579 | 397.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 242 | 276.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 579 | 575.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okunoda_miyoi_touhou/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/okunoda_miyoi_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blue_shirt, looking_at_viewer, simple_background, solo, white_background, long_sleeves, purple_skirt, smile, blush, closed_mouth, fish_print, gourd, holding, white_shirt, standing | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blue_shirt, fish_print, looking_at_viewer, purple_skirt, short_sleeves, simple_background, solo, blue_apron, holding, open_mouth, :d, white_background, blush, cross-laced_clothes, ofuda_on_clothes | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blue_shirt, holding_tray, open_mouth, purple_skirt, solo, white_background, apron, fish_print, bottle, ofuda_on_clothes, short_sleeves, simple_background, tokkuri, gourd, looking_at_viewer, :d, blush, choko_(cup), full_body | | 3 | 16 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blue_shirt, looking_at_viewer, solo, blush, short_sleeves, open_mouth, simple_background, white_background, upper_body, :d, purple_skirt, holding | | 4 | 16 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, 1boy, hetero, solo_focus, nipples, penis, open_mouth, looking_at_viewer, paizuri, smile, upper_body, mosaic_censoring, sweat, cum_on_body, facial, huge_breasts, indoors, nude, one_eye_closed | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_shirt | looking_at_viewer | simple_background | solo | white_background | long_sleeves | purple_skirt | smile | blush | closed_mouth | fish_print | gourd | holding | white_shirt | standing | short_sleeves | blue_apron | open_mouth | :d | cross-laced_clothes | ofuda_on_clothes | holding_tray | apron | bottle | tokkuri | choko_(cup) | full_body | upper_body | 1boy | hetero | solo_focus | nipples | penis | paizuri | mosaic_censoring | sweat | cum_on_body | facial | huge_breasts | indoors | nude | one_eye_closed | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------------------|:--------------------|:-------|:-------------------|:---------------|:---------------|:--------|:--------|:---------------|:-------------|:--------|:----------|:--------------|:-----------|:----------------|:-------------|:-------------|:-----|:----------------------|:-------------------|:---------------|:--------|:---------|:----------|:--------------|:------------|:-------------|:-------|:---------|:-------------|:----------|:--------|:----------|:-------------------|:--------|:--------------|:---------|:---------------|:----------|:-------|:-----------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | X | | X | | X | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | | X | | X | | X | X | | | | X | | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 3 | 16 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | X | | X | | X | | | | X | | | X | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | 4 | 16 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | | | | | | X | X | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

rdcoder/finetune-test-1

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 912758 num_examples: 404 download_size: 425762 dataset_size: 912758 configs: - config_name: default data_files: - split: train path: data/train-* ---

BangumiBase/punchline

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Punch Line This is the image base of bangumi Punch Line, we detected 17 characters, 1203 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 104 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 12 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 134 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 47 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 135 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 16 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 11 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 14 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 14 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 21 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 18 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 40 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 150 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 324 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 14 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 9 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | noise | 140 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

kobkrit/rd-taxqa

--- license: apache-2.0 ---

valituttiromero/Billy

--- license: openrail ---

eswardivi/IndicVoices

--- dataset_info: - config_name: telugu_train features: - name: audio dtype: audio - name: end dtype: float64 - name: text dtype: string - name: start dtype: float64 - name: speaker_id dtype: int64 - name: audio_raw_duration dtype: float64 - name: scenario dtype: string - name: task_name dtype: string splits: - name: split1 num_bytes: 1609730034.0 num_examples: 1000 - name: split2 num_bytes: 1049782097.0 num_examples: 1000 - name: split3 num_bytes: 1444386250.0 num_examples: 1000 - name: split4 num_bytes: 3152422807.0 num_examples: 1000 - name: split5 num_bytes: 5554666300.0 num_examples: 1000 - name: split6 num_bytes: 4003049531.0 num_examples: 500 - name: split7 num_bytes: 4053228693.0 num_examples: 500 - name: split8 num_bytes: 1044856061.0 num_examples: 344 - name: split9 num_bytes: 5059082700.0 num_examples: 500 - name: split10 num_bytes: 4664957526.0 num_examples: 500 download_size: 29946910673 dataset_size: 31636161999.0 - config_name: telugu_valid features: - name: audio dtype: audio - name: text dtype: string - name: audio_raw_duration dtype: float64 - name: scenario dtype: string - name: task_name dtype: string splits: - name: valid num_bytes: 314053800.0 num_examples: 73 download_size: 301914962 dataset_size: 314053800.0 configs: - config_name: telugu_train data_files: - split: split1 path: telugu_train/split1-* - split: split2 path: telugu_train/split2-* - split: split3 path: telugu_train/split3-* - split: split4 path: telugu_train/split4-* - split: split5 path: telugu_train/split5-* - split: split6 path: telugu_train/split6-* - split: split7 path: telugu_train/split7-* - split: split8 path: telugu_train/split8-* - split: split9 path: telugu_train/split9-* - split: split10 path: telugu_train/split10-* - config_name: telugu_valid data_files: - split: valid path: telugu_valid/valid-* --- This dataset is processed from [indicvoices](https://ai4bharat.iitm.ac.in/indicvoices/)

vikp/pypi_labeled

--- dataset_info: features: - name: code dtype: string - name: package dtype: string - name: path dtype: string - name: filename dtype: string - name: parsed_code dtype: string - name: quality_prob dtype: float64 - name: learning_prob dtype: float64 splits: - name: train num_bytes: 40005369487 num_examples: 1902405 download_size: 11174800633 dataset_size: 40005369487 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "pypi_labeled" All of the latest package versions from pypi. The original data came from [here](https://py-code.org/datasets). I pulled the latest versions of each package, then extracted only `md`, `rst`, `ipynb`, and `py` files. I then applied some cleaning: - rendering notebooks - removing leading comments/licenses Then filtered out some low-quality code, and labeled the rest according to learning value and quality. Subset by those columns to get higher quality code.

beenow011/quill

--- license: unknown ---

WillHeld/SD-QA

--- dataset_info: features: - name: id dtype: string - name: aus dtype: audio - name: gbr dtype: audio - name: ind_n dtype: audio - name: ind_s dtype: audio - name: irl dtype: audio - name: kenya dtype: audio - name: nga dtype: audio - name: nzl dtype: audio - name: phl dtype: audio - name: usa dtype: audio - name: zaf dtype: audio - name: answers sequence: string - name: question dtype: string splits: - name: dev num_bytes: 1605295564.0 num_examples: 1000 - name: test num_bytes: 1687465403.375 num_examples: 1031 download_size: 3028852311 dataset_size: 3292760967.375 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* ---

arnepeine/medspeech2

--- license: other ---

mteb/swerec_classification

--- language: - sv dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 355633 num_examples: 1024 - name: test num_bytes: 713970 num_examples: 2048 - name: val num_bytes: 82442 num_examples: 256 download_size: 697285 dataset_size: 1152045 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: val path: data/val-* ---

autoevaluate/autoeval-staging-eval-billsum-default-dd3eba-14585981

--- type: predictions tags: - autotrain - evaluation datasets: - billsum eval_info: task: summarization model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2 metrics: [] dataset_name: billsum dataset_config: default dataset_split: test col_mapping: text: text target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2 * Dataset: billsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

nibbz2024/SurveySense-datasetv1

--- task_categories: - text-generation language: - en ---

rntc/blurb_bc5disease_a-tm

--- dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: type dtype: string - name: ner_tags sequence: class_label: names: '0': O '1': B '2': I splits: - name: train num_bytes: 16267150 num_examples: 4560 - name: validation num_bytes: 15854894 num_examples: 4581 - name: test num_bytes: 16855285 num_examples: 4797 download_size: 6974702 dataset_size: 48977329 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

gilkeyio/librispeech-alignments

--- dataset_info: features: - name: sex dtype: string - name: subset dtype: string - name: id dtype: string - name: audio dtype: audio - name: transcript dtype: string - name: words list: - name: end dtype: float64 - name: start dtype: float64 - name: word dtype: string - name: phonemes list: - name: end dtype: float64 - name: phoneme dtype: string - name: start dtype: float64 splits: - name: dev_clean num_bytes: 365310608.879 num_examples: 2703 - name: dev_other num_bytes: 341143993.784 num_examples: 2864 - name: test_clean num_bytes: 377535532.98 num_examples: 2620 - name: test_other num_bytes: 351207892.569557 num_examples: 2938 - name: train_clean_100 num_bytes: 6694747231.610863 num_examples: 28538 - name: train_clean_360 num_bytes: 24163659711.787865 num_examples: 104008 - name: train_other_500 num_bytes: 32945085271.89443 num_examples: 148645 download_size: 62101682957 dataset_size: 65238690243.50571 configs: - config_name: default data_files: - split: dev_clean path: data/dev_clean-* - split: dev_other path: data/dev_other-* - split: test_clean path: data/test_clean-* - split: test_other path: data/test_other-* - split: train_clean_100 path: data/train_clean_100-* - split: train_clean_360 path: data/train_clean_360-* - split: train_other_500 path: data/train_other_500-* license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - en pretty_name: Librispeech Alignments size_categories: - 100K<n<1M --- # Dataset Card for Librispeech Alignments Librispeech with alignments generated by the [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/en/latest/). The original alignments in TextGrid format can be found [here](https://zenodo.org/records/2619474) ## Dataset Details ### Dataset Description Librispeech is a corpus of read English speech, designed for training and evaluating automatic speech recognition (ASR) systems. The dataset contains 1000 hours of 16kHz read English speech derived from audiobooks. The Montreal Forced Aligner (MFA) was used to generate word and phoneme level alignments for the Librispeech dataset. - **Curated by:** Vassil Panayotov, Guoguo Chen, Daniel Povey, Sanjeev Khudanpur (for Librispeech) - **Funded by:** DARPA LORELEI - **Shared by:** Loren Lugosch (for Alignments) - **Language(s) (NLP):** English - **License:** Creative Commons Attribution 4.0 International License ### Dataset Sources - **Repository:** https://www.openslr.org/12 - **Paper:** https://arxiv.org/abs/1512.02595 - **Alignments:** https://zenodo.org/record/2619474 ## Uses ### Direct Use The Librispeech dataset can be used to train and evaluate ASR systems. The alignments allow for forced alignment techniques. ### Out-of-Scope Use The dataset only contains read speech, so may not perform as well on spontaneous conversational speech. ## Dataset Structure The dataset contains 1000 hours of segmented read English speech from audiobooks. There are three train subsets: 100 hours (train-clean-100), 360 hours (train-clean-360) and 500 hours (train-other-500). The alignments connect the audio to the reference text transcripts on word and phoneme level. ### Data Fields - sex: M for male, F for female - subset: dev_clean, dev_other, test_clean, test_other, train_clean_100, train_clean_360, train_other_500 - id: unique id of the data sample. (speaker id)-(chapter-id)-(utterance-id) - audio: the audio, 16kHz - transcript: the spoken text of the dataset, normalized and lowercased - words: a list of words with fields: - word: the text of the word - start: the start time in seconds - end: the end time in seconds - phonemes: a list of phonemes with fields: - phoneme: the phoneme spoken - start: the start time in seconds - end: the end time in seconds ## Dataset Creation ### Curation Rationale Librispeech was created to further speech recognition research and to benchmark progress in the field. ### Source Data #### Data Collection and Processing The audio and reference texts were sourced from read English audiobooks in the LibriVox project. The data was segmented, filtered and prepared for speech recognition. #### Who are the source data producers? The audiobooks are read by volunteers for the LibriVox project. Information about the readers is available in the LibriVox catalog. ### Annotations #### Annotation process The Montreal Forced Aligner was used to create word and phoneme level alignments between the audio and reference texts. The aligner is based on Kaldi. In the process of formatting this into a HuggingFace dataset, words with empty text and phonemes with empty text, silence tokens, or spacing tokens were removed #### Who are the annotators? The alignments were generated automatically by the Montreal Forced Aligner and shared by Loren Lugosch. The TextGrid files were parsed and integrated into this dataset by Kim Gilkey. #### Personal and Sensitive Information The data contains read speech and transcripts. No personal or sensitive information expected. ## Bias, Risks, and Limitations The dataset contains only read speech from published books, not natural conversational speech. Performance on other tasks may be reduced. ### Recommendations Users should understand that the alignments may contain errors and account for this in applications. For example, be wary of <UNK> tokens. ## Citation **Librispeech:** ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={ICASSP}, year={2015}, organization={IEEE} } ``` **Librispeech Alignments:** ``` Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, "Speech Model Pre-training for End-to-End Spoken Language Understanding", Interspeech 2019. ``` **Montreal Forced Aligner:** ``` Michael McAuliffe, Michaela Socolof, Sarah Mihuc, Michael Wagner, and Morgan Sonderegger. "Montreal Forced Aligner: trainable text-speech alignment using Kaldi", Interspeech 2017. ```

yzhuang/autotree_automl_california_sgosdt_l256_d3_sd0

--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 185200000 num_examples: 10000 - name: validation num_bytes: 185200000 num_examples: 10000 download_size: 149978405 dataset_size: 370400000 --- # Dataset Card for "autotree_automl_california_sgosdt_l256_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Seongill/SQuAD_unique_questions

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: masked_query dtype: string - name: query_embedding sequence: float64 splits: - name: train num_bytes: 697771735 num_examples: 97888 download_size: 506779114 dataset_size: 697771735 configs: - config_name: default data_files: - split: train path: data/train-* ---

Yashaswat/Indian-Legal-Text-ABS

--- dataset_info: features: - name: judgement dtype: string - name: summary dtype: string splits: - name: train num_bytes: 106644414 num_examples: 3599 - name: test num_bytes: 3260335 num_examples: 100 - name: validation num_bytes: 26921474 num_examples: 900 download_size: 66712543 dataset_size: 136826223 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

njkumarr/humanevalinfilljava

--- language: - en pretty_name: HumanEval-Infilling Java --- Dataset for human eval infill for java, based on https://arxiv.org/pdf/2207.14255

mesolitica/mixtral-malaysian-rag

--- license: mit language: - ms --- # Mixtral Malaysian RAG Simulate Multi-turn RAG conversations, some questions purposely asked out-of-context and the users throw random rubbish replies. notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/mixtral-malaysian-rag - [rag-multiturn-chaotic.jsonl](rag-multiturn-chaotic.jsonl), 327980 rows, 4.1 GB - [rag-multiturn-chaotic-part2.jsonl](rag-multiturn-chaotic-part2.jsonl), 169403 rows, 2.45 GB - [mixtral-rag-question-disagree.jsonl](mixtral-rag-question-disagree.jsonl), 339627 rows, 3.08 GB ## Example data ```python {'filename': 'gov.my.jsonl', 'chat': [{'role': 'user', 'content': "Are you as sure as a horse's hind leg?", 'content_ms': 'Adakah anda pasti seperti kaki belakang kuda?'}, {'role': 'assistant', 'content': "I'm afraid I don't understand the reference to a horse's hind leg in relation to my ability to provide accurate information. However, I can assure you that I will provide information based solely on the knowledge base you've given me and to the best of my ability, I will provide accurate and relevant responses. I don't have feelings, beliefs, or personal knowledge, I am a machine learning model that generate text based on the input data.", 'content_ms': 'Saya takut saya tidak memahami rujukan kepada kaki belakang kuda berkaitan dengan keupayaan saya untuk memberikan maklumat yang tepat. Walau bagaimanapun, saya boleh memberi jaminan kepada anda bahawa saya akan memberikan maklumat hanya berdasarkan pangkalan pengetahuan yang anda berikan kepada saya dan setakat kemampuan saya, saya akan memberikan respons yang tepat dan relevan. Saya tidak mempunyai perasaan, kepercayaan atau pengetahuan peribadi, saya ialah model pembelajaran mesin yang menjana teks berdasarkan data input.'}, {'role': 'user', 'content': 'When does the order come into force?', 'content_ms': 'Bilakah perintah itu mula berkuat kuasa?'}, {'role': 'assistant', 'content': 'Based on the knowledge base provided, the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989 comes into force on the 1st of May, 1989.', 'content_ms': 'Berdasarkan pangkalan pengetahuan yang disediakan, Perintah Kualiti Alam Sekeliling (Premis Ditetapkan) (Kemudahan Rawatan dan Pelupusan Sisa Terjadual) 1989 berkuat kuasa pada 1 Mei 1989.'}, {'role': 'user', 'content': 'How does the order impact businesses and individuals in Malaysia?', 'content_ms': 'Bagaimanakah pesanan itu memberi kesan kepada perniagaan dan individu di Malaysia?'}, {'role': 'assistant', 'content': 'Based on the knowledge base provided, the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989, which comes into force on the 1st of May, 1989 in Malaysia, is a subsidiary legislation under the Environmental Quality Act 1974 [Act 127]. This order sets forth the regulations for prescribed premises and scheduled wastes treatment and disposal facilities in Malaysia. The aim of this order is to control and manage the generation, treatment, storage, and disposal of scheduled wastes in a manner that protects the environment and human health.\n\nTherefore, businesses and individuals in Malaysia that generate, handle, or dispose of scheduled wastes, as defined in the order, will be impacted by these regulations. They will be required to comply with the standards and requirements set forth in the order, such as proper treatment, storage, and disposal of scheduled wastes, and obtaining the necessary permits and approvals from the relevant authorities. Non-compliance with the regulations can result in penalties, such as fines and/or imprisonment. It is important for businesses and individuals in Malaysia to be aware of and understand the requirements of this order, and to take appropriate measures to ensure compliance.', 'content_ms': 'Berdasarkan pangkalan pengetahuan yang disediakan, Perintah Kualiti Alam Sekeliling (Premis Ditetapkan) (Kemudahan Rawatan dan Pelupusan Sisa Terjadual) 1989, yang berkuat kuasa pada 1 Mei, 1989 di Malaysia, adalah perundangan subsidiari di bawah Akta Kualiti Alam Sekitar 1974 [ Akta 127]. Perintah ini menetapkan peraturan untuk premis yang ditetapkan dan kemudahan rawatan dan pelupusan sisa terjadual di Malaysia. Matlamat perintah ini adalah untuk mengawal dan mengurus penjanaan, rawatan, penyimpanan dan pelupusan sisa terjadual dengan cara yang melindungi alam sekitar dan kesihatan manusia.\n\nOleh itu, perniagaan dan individu di Malaysia yang menjana, mengendalikan atau membuang sisa terjadual, seperti yang dinyatakan dalam perintah itu, akan terjejas oleh peraturan ini. Mereka akan dikehendaki mematuhi piawaian dan keperluan yang ditetapkan dalam perintah itu, seperti rawatan yang betul, penyimpanan, dan pelupusan sisa terjadual, dan mendapatkan permit dan kelulusan yang diperlukan daripada pihak berkuasa yang berkaitan. Ketidakpatuhan terhadap peraturan boleh mengakibatkan penalti, seperti denda dan/atau penjara. Adalah penting bagi perniagaan dan individu di Malaysia untuk menyedari dan memahami keperluan perintah ini, dan mengambil langkah yang sesuai untuk memastikan pematuhan.'}, {'role': 'user', 'content': 'What are the potential consequences for non-compliance with the order?', 'content_ms': 'Apakah akibat yang berpotensi untuk ketidakpatuhan terhadap perintah itu?'}, {'role': 'assistant', 'content': "Based on the knowledge base provided, the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989 is a subsidiary legislation under the Environmental Quality Act 1974 in Malaysia. The order sets forth the regulations for prescribed premises and scheduled wastes treatment and disposal facilities in Malaysia.\n\nNon-compliance with the regulations set forth in the order can result in penalties, such as fines and/or imprisonment. The exact penalties for non-compliance are not specified in the order, but are likely defined in the Environmental Quality Act 1974.\n\nIt's important to note that I'm a language model, and my knowledge cut off is 2021, I don't have real-time access to any laws, regulations or any other legal information. It is always recommended to consult the legal text or consult with a legal professional to get the most accurate and up-to-date information about the potential consequences of non-compliance with any specific regulation.", 'content_ms': 'Berdasarkan pangkalan pengetahuan yang disediakan, Perintah Kualiti Alam Sekeliling (Premis Ditetapkan) (Kemudahan Rawatan dan Pelupusan Sisa Terjadual) 1989 adalah perundangan subsidiari di bawah Akta Kualiti Alam Sekeliling 1974 di Malaysia. Perintah itu menetapkan peraturan untuk premis yang ditetapkan dan kemudahan rawatan dan pelupusan sisa terjadual di Malaysia.\n\nKetidakpatuhan terhadap peraturan yang ditetapkan dalam perintah itu boleh mengakibatkan penalti, seperti denda dan/atau penjara. Penalti yang tepat bagi ketidakpatuhan tidak dinyatakan dalam perintah itu, tetapi mungkin ditakrifkan dalam Akta Kualiti Alam Sekeliling 1974.\n\nAdalah penting untuk ambil perhatian bahawa saya adalah model bahasa, dan pengetahuan saya terputus ialah 2021, saya tidak mempunyai akses masa nyata kepada mana-mana undang-undang, peraturan atau sebarang maklumat undang-undang lain. Ia sentiasa disyorkan untuk merujuk teks undang-undang atau berunding dengan profesional undang-undang untuk mendapatkan maklumat yang paling tepat dan terkini tentang kemungkinan akibat ketidakpatuhan terhadap mana-mana peraturan tertentu.'}, {'role': 'user', 'content': "How does the order contribute to Malaysia's overall environmental quality and sustainability efforts?", 'content_ms': 'Bagaimanakah perintah itu menyumbang kepada usaha kualiti alam sekitar dan kemampanan keseluruhan Malaysia?'}, {'role': 'assistant', 'content': "Based on the provided knowledge base, the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989 is a subsidiary legislation under the Environmental Quality Act 1974 in Malaysia. The order sets forth the regulations for prescribed premises and scheduled wastes treatment and disposal facilities in Malaysia, which aim to control and manage the generation, treatment, storage, and disposal of scheduled wastes in a manner that protects the environment and human health. By establishing standards and requirements for the proper handling and disposal of scheduled wastes, the order helps to prevent pollution, protect the environment, and promote sustainability. This order is one of the regulations put in place by the Malaysian government to control and manage the waste generated by different industries and businesses, in order to achieve a good environmental quality and a sustainable development.\n\nAdditionally, The Environmental Quality Act 1974 is a primary legislation that provides the legal framework for environmental protection, conservation and management in Malaysia, which aim to protect and improve the quality of the environment, and to provide for the prevention, abatement and control of pollution and environmental degradation. The order contributes to Malaysia's overall environmental quality and sustainability efforts by setting specific regulations that must be followed, thus ensuring a better and more effective enforcement of the general objectives of the act.\n\nHowever, I'd like to point out again that my knowledge cut off is 2021 and I am a machine learning model, and my response is based on the provided knowledge base, and I do not have real-time access to any laws, regulations or any other legal information. It is always recommended to consult the legal text or consult with a legal professional to get the most accurate and up-to-date information about the impact of the order on Malaysia's overall environmental quality and sustainability efforts.", 'content_ms': 'Berdasarkan pangkalan pengetahuan yang disediakan, Perintah Kualiti Alam Sekeliling (Premis Ditetapkan) (Kemudahan Rawatan dan Pelupusan Sisa Terjadual) 1989 ialah perundangan subsidiari di bawah Akta Kualiti Alam Sekeliling 1974 di Malaysia. Perintah itu menetapkan peraturan untuk premis yang ditetapkan dan kemudahan rawatan dan pelupusan sisa terjadual di Malaysia, yang bertujuan untuk mengawal dan mengurus penjanaan, rawatan, penyimpanan, dan pelupusan sisa terjadual dengan cara yang melindungi alam sekitar dan kesihatan manusia. Dengan mewujudkan piawaian dan keperluan untuk pengendalian dan pelupusan sisa terjadual yang betul, perintah itu membantu mencegah pencemaran, melindungi alam sekitar dan menggalakkan kemampanan. Perintah ini adalah salah satu peraturan yang ditetapkan oleh kerajaan Malaysia untuk mengawal dan mengurus sisa yang dihasilkan oleh industri dan perniagaan yang berbeza, untuk mencapai kualiti alam sekitar yang baik dan pembangunan yang mampan.\n\nDi samping itu, Akta Kualiti Alam Sekeliling 1974 ialah perundangan utama yang menyediakan rangka kerja perundangan untuk perlindungan, pemuliharaan dan pengurusan alam sekitar di Malaysia, yang bertujuan untuk melindungi dan meningkatkan kualiti alam sekitar, dan untuk memperuntukkan pencegahan, pengurangan dan kawalan pencemaran. dan kemerosotan alam sekitar. Perintah itu menyumbang kepada keseluruhan usaha kualiti alam sekitar dan kemampanan Malaysia dengan menetapkan peraturan khusus yang mesti dipatuhi, sekali gus memastikan penguatkuasaan yang lebih baik dan berkesan terhadap objektif am akta tersebut.\n\nWalau bagaimanapun, saya ingin menegaskan sekali lagi bahawa pengetahuan saya terputus ialah 2021 dan saya adalah model pembelajaran mesin, dan respons saya adalah berdasarkan pangkalan pengetahuan yang disediakan, dan saya tidak mempunyai akses masa nyata kepada mana-mana undang-undang, peraturan. atau sebarang maklumat undang-undang lain. Ia sentiasa disyorkan untuk merujuk teks undang-undang atau berunding dengan profesional undang-undang untuk mendapatkan maklumat yang paling tepat dan terkini tentang kesan perintah itu terhadap keseluruhan usaha kualiti alam sekitar dan kelestarian Malaysia.'}], 'context': 'and Chen, J (2013). Integration of high-resolution physical and genetic map reveals differential recombination frequency between chromosomes and the genome assembling quality in cucumber. PLOS ONE 8(5): e62676. Madon, M; Arulandoo, X; Sritharan, K; Nordiana, H M N; Muhammad Azwan, Z and Zaki, N M (2018). Short communication: Genomic constitution of oil palm interspecific hybrid crosses monitored by genomic in situ hybridization (GISH). J. Oil Palm Res. Vol. 30(2): 338-344. Madon, M; Clyde, M M and Cheah, S C (1999). Application of genomic in situ hybridization (GISH) on Elaeis hybrids. J. Oil Palm Res. Vol. (Special Issue): 74-80. Madon, M; Heslop-Harrison, J S; Schwarzacher, T; Mohd Rafdi, M H and Clyde, M M (2005). Cytological analysis of oil palm pollen mother cells (PMC). J. Oil Palm Res. Vol. 17: 176-180. Maluszynska, J and Hasterok, R (2005). Identification of individual chromosomes and parental genomes in Brassica juncea using GISH and FISH. Cyto. and Genome Res. 109: 310-314. Meunier, J (1987). Prospects of E. oleifera x E. guineensis interspecific hybrids. IRHO results. Proc. of ISOPB/PORIM Workshop on Prospects of Interspecific Hybrids (Soh, A C and Rajanaidu, N eds.). p. 11-15. Murphy, S P and Bass, H W (2012). Genetics and cytology of meiotic chromosome behavior in plants. Plant Cytogenetics, Plant Genetics and Genomics: Crops and Models 4 (Bass, H W and Birchler, J A eds.). 345 pp. DOI:10.1007/978-0-387-70869-0_8. Nair, P K K (1969). The thesis of pollen based plant research. J. Palynol., 5(1): 49-50. Nordiana, H M N; Muhammad Azwan, Z; Arulandoo, X; Sritharan, K and Madon, M (2016). Development of meiotic pachytene chromosomes preparation technique in oil palm interspecific hybrid and backcross (BC2), Transactions of Persatuan Genetik Malaysia Vol. 3. Genetics Society of Malaysia. p. 143-146. Phillips, D; Wnetrzak, J; Nibau, C; Barakate, A; Ramsay, L; Wright, F; Higgins, J D, Perry, R M and Jenkins, G (2013). Quantitative high resolution mapping of HvMLH3 foci in barley pachytene nuclei reveals a strong distal bias and weak interference. J. Exp. Bot., 64(8): 2139-2154. DOI:10.1093/jxb/ert079. Rahimah, A B; Cheah, S C and Singh, R (2006). Freeze-drying of oil palm (Elaeis guineensis) leaf and its effect on the quality of extractable DNA. J. Oil Palm Res. Vol. 18: 296-304. Rajanaidu, N; Chin, C W and Jalani, B S (1995). Performance of Elaeis oleifera (Surinam) x E. guineensis hybrids. ISOPB International Seminar. Barranquilla, Colombia. Rasheed, A A; Perveen, A; Abid, R and Qaiser, M (2016). Pollen morphology of the subfamily Arecoideae Griff. (family-Arecaceae) from Pakistan and Kashmir. Pak. J. Bot., 48(3): 1051-1060. Ravi Kumar, C and Nair, P K K (1986). Inheritance of exine ornamentation and pollen shape in the interspecific tetraploid hybrids of Gloriosa. Can. J. Bot., 64: 3134-3140. Rhee, H K; Cho, H R and Kim, K S (2005). Comparison of pollen morphology in interspecific hybrid lilies after in vitro chromosome doubling. Proc. of the IXth International Symposium on Flower Bulbs. Acta Hot., 673: 639-643. Schubert, I; Fransz, P F; Fuchs, J and De Jong, J H (2001). Chromosome painting in plants. Methods in Cell Science, 23(1-3): 57-69. Schwarzacher, T and Heslop-Harrison, J S (2000). Practical in situ hybridization. BIOS Scientific Publishers Ltd, Abindgdon, United Kingdom. 250 pp. Schwarzacher, T; Anamthawat-Jonsson, K and Harrison, G E (1992). Genomic in situ hybridization to identify alien chromosomes and chromosome segments in wheat. Theor. Appl. Genet., 84: 778-786. Singh, R; Ong-Abdullah, M; Low, E T L; Manaf, M A A; Rosli, R; Rajanaidu, N; Ooi, L C L; Ooi, S E; Chan, K-L; Ab Halim, M A; Azizi, N; Jayanthi, N; Bacher, B; Lakey, N; Steven, W S; He, D; Hogan, M; Budiman, M A; Lee, E K; Desalle, R; Kudma, D; Goicoechea, J L; Wing, R A; Wilson, R K; Fulton, R S; Ordway, J M; 11 OIL PALM MALE MEIOSIS PROFILING: FROM FIELD OBSERVATION TO CYTOGENETICS ANALYSIS Martienssen, R A and Sambanthamurthi, R (2013). Oil palm genome sequence reveals divergence of interfertile species in Old and New worlds. Nature, 500: 335-339. Thomas, P T and Thomas, H (1973). Annual Report of the Welsh Plant Breeding Station for 1973. Aberystwyth, Cambrian News. 85 pp. Zhong, X B; De Jong, J H and Zabel, P (1996). Preparation of tomato meiotic pachytene and mitotic metaphase chromosomes suitable for fluorescence in situ hybridization (FISH). Chromosome Res., 4: 24-28. FEDERAL SUBSIDIARY LEGISLATION ENVIRONMENTAL QUALITY ACT 1974 [ACT 127] P.U.(A) 140/89 ENVIRONMENTAL QUALITY (PRESCRIBED PREMISES) (SCHEDULED WASTES TREATMENT AND DISPOSAL FACILITIES) ORDER 1989 Incorporating latest amendments - P.U.(A) 252/2006 Publication : 27th April 1989 Date of coming into operation : 1st May 1989 ARRANGEMENT OF ORDERS Preamble Order 1. Citation and commencement. Order 2. Interpretation. Order 3. Prescribed premises. LIST OF AMENDMENTS. Preamble IN exercise of the powers conferred by section 18 of the Environmental Quality Act 1974 [Act 127], the Minister, after consultation with the Environmental Quality Council, makes the following order : Order 1. Citation and commencement. This order may be cited as the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989 and shall come into force on the 1st May 1989.', 'questions': ['What is the purpose of the Environmental Quality Act 1974 and the Environmental Quality (Prescribed Premises) (Scheduled Wastes Treatment and Disposal Facilities) Order 1989?', 'What is the role of the Minister and the Environmental Quality Council in creating the order?', 'What premises are prescribed in the order?', 'When does the order come into force?', 'How are terms in the order defined in Order 2?', 'What is the significance of the amendments listed in the LIST OF AMENDMENTS section?', 'What other federal subsidiary legislation relates to environmental quality in Malaysia?', 'How does the Environmental Quality Act 1974 relate to other environmental legislation in Malaysia?', 'How does the order fit into the broader context of environmental regulations in Malaysia?', 'How does the order compare to similar regulations in other countries?', 'How is compliance with the order enforced?', 'What are the potential consequences for non-compliance with the order?', 'Are there any notable court cases or legal precedents related to the order?', 'How does the order impact businesses and individuals in Malaysia?', "How does the order contribute to Malaysia's overall environmental quality and sustainability efforts?"]} ```

ZahraAlharz/alriyadh3

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: content dtype: string - name: url dtype: string - name: date dtype: string - name: title dtype: string splits: - name: train num_bytes: 6387 num_examples: 3 download_size: 20826 dataset_size: 6387 --- # Dataset Card for "alriyadh3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

synthseq/circuits

--- license: mit ---

RJuro/go-emotion-dk-autotranlated-10k

--- dataset_info: features: - name: text_en dtype: string - name: text dtype: string - name: labels dtype: class_label: names: '0': admiration '1': amusement '2': anger '3': annoyance '4': approval '5': caring '6': confusion '7': curiosity '8': desire '9': disappointment '10': disapproval '11': disgust '12': embarrassment '13': excitement '14': fear '15': gratitude '16': grief '17': joy '18': love '19': nervousness '20': neutral '21': optimism '22': pride '23': realization '24': relief '25': remorse '26': sadness '27': surprise - name: __index_level_0__ dtype: int64 - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 2916184 num_examples: 9000 - name: test num_bytes: 159317 num_examples: 500 - name: valid num_bytes: 162379 num_examples: 500 download_size: 1662215 dataset_size: 3237880 --- # Dataset Card for "go-emotion-dk-autotranlated-10k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

VivekNaga/Medicaldata

--- license: apache-2.0 ---

grakky1510/growheads

--- license: apache-2.0 ---

nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io

--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m1_fine_tuning_ocr_ptrn_cmbert_io ## Introduction This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century. ## Dataset parameters * Approach : M1 * Dataset type : noisy (Pero OCR) * Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) * Tagging format : IO * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned models : * Level-1 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1) * Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2) ## Entity types Abbreviation|Entity group (level)|Description -|-|- O |1 & 2|Outside of a named entity PER |1|Person or company name ACT |1 & 2|Person or company professional activity TITREH |2|Military or civil distinction DESC |1|Entry full description TITREP |2|Professionnal reward SPAT |1|Address LOC |2|Street name CARDINAL |2|Street number FT |2|Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io")

i2/cnn_models

--- dataset_info: features: - name: code dtype: string splits: - name: train num_bytes: 2708721.9 num_examples: 4500 - name: test num_bytes: 300969.1 num_examples: 500 download_size: 61959 dataset_size: 3009691.0 --- # Dataset Card for "cnn_models" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

1NightRaid1/testing

--- license: apache-2.0 task_categories: - question-answering tags: - code size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Paulitos/school-math-questions-llama2-5k

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2721591 num_examples: 5000 download_size: 1363904 dataset_size: 2721591 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/ticonderoga_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ticonderoga/タイコンデロガ/提康德罗加 (Azur Lane) This is the dataset of ticonderoga/タイコンデロガ/提康德罗加 (Azur Lane), containing 55 images and their tags. The core tags of this character are `blue_eyes, breasts, long_hair, large_breasts, blue_hair, hair_ornament, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 55 | 81.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 55 | 44.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 139 | 97.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 55 | 71.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 139 | 139.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ticonderoga_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/ticonderoga_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, blue_bikini, cleavage, looking_at_viewer, smile, thigh_strap, o-ring_bikini, eyewear_on_head, sunglasses, beach, navel, skindentation, bare_shoulders, collarbone, outdoors, sitting, blush, closed_mouth, day, open_clothes, see-through, alternate_costume, black_choker, bracelet, food, stomach, thighs | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, fingerless_gloves, looking_at_viewer, elbow_gloves, solo, red_necktie, coat_on_shoulders, black_thighhighs, skirt, smile, dress, open_coat, simple_background | | 2 | 16 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, looking_at_viewer, solo, fake_animal_ears, playboy_bunny, rabbit_ears, detached_collar, official_alternate_costume, wrist_cuffs, cleavage, strapless_leotard, two-tone_legwear, very_long_hair, bare_shoulders, red_bowtie, navel_cutout, striped_thighhighs, purple_thighhighs, white_background, black_footwear, cup, high_heels, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blue_bikini | cleavage | looking_at_viewer | smile | thigh_strap | o-ring_bikini | eyewear_on_head | sunglasses | beach | navel | skindentation | bare_shoulders | collarbone | outdoors | sitting | blush | closed_mouth | day | open_clothes | see-through | alternate_costume | black_choker | bracelet | food | stomach | thighs | fingerless_gloves | elbow_gloves | red_necktie | coat_on_shoulders | black_thighhighs | skirt | dress | open_coat | simple_background | fake_animal_ears | playboy_bunny | rabbit_ears | detached_collar | official_alternate_costume | wrist_cuffs | strapless_leotard | two-tone_legwear | very_long_hair | red_bowtie | navel_cutout | striped_thighhighs | purple_thighhighs | white_background | black_footwear | cup | high_heels | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------|:-----------|:--------------------|:--------|:--------------|:----------------|:------------------|:-------------|:--------|:--------|:----------------|:-----------------|:-------------|:-----------|:----------|:--------|:---------------|:------|:---------------|:--------------|:--------------------|:---------------|:-----------|:-------|:----------|:---------|:--------------------|:---------------|:--------------|:--------------------|:-------------------|:--------|:--------|:------------|:--------------------|:-------------------|:----------------|:--------------|:------------------|:-----------------------------|:--------------|:--------------------|:-------------------|:-----------------|:-------------|:---------------|:---------------------|:--------------------|:-------------------|:-----------------|:------|:-------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 2 | 16 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

AnyaSaphe/killjoy

--- license: openrail ---

CyberHarem/amakusa_shino_seitokaiyakuindomo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Amakusa Shino (Seitokai Yakuindomo) This is the dataset of Amakusa Shino (Seitokai Yakuindomo), containing 972 images and their tags. The core tags of this character are `long_hair, black_hair, brown_eyes, bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 972 | 479.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 972 | 410.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1920 | 764.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 972 | 478.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1920 | 869.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amakusa_shino_seitokaiyakuindomo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/amakusa_shino_seitokaiyakuindomo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blazer, school_uniform, solo, upper_body, purple_eyes | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blazer, school_uniform, solo | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blazer, school_uniform, solo, profile, black_eyes | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, armband, blazer, school_uniform, solo, smile | | 4 | 18 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, school_uniform, solo, blazer, plaid_skirt | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, blazer, bowtie, hair_between_eyes, looking_at_viewer, school_uniform, solo, upper_body, red_bow | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, parody, school_uniform, solo, anime_coloring | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, chair, desk, school_uniform, sitting, solo, classroom, profile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blazer | school_uniform | solo | upper_body | purple_eyes | profile | black_eyes | armband | smile | plaid_skirt | bowtie | hair_between_eyes | looking_at_viewer | red_bow | parody | anime_coloring | chair | desk | sitting | classroom | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-----------------|:-------|:-------------|:--------------|:----------|:-------------|:----------|:--------|:--------------|:---------|:--------------------|:--------------------|:----------|:---------|:-----------------|:--------|:-------|:----------|:------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | | | | | | | | | | | | | | | | | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | | X | X | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | | | | | X | X | | | | | | | | | | | | | 4 | 18 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | | | | | | | X | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | X | | | | | | | X | X | X | X | | | | | | | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | X | X | | | | | | | | | | | | X | X | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | X | X | | | X | | | | | | | | | | | X | X | X | X |

syeddhasnainn/landslide-segmentation-dataset-512-512

--- dataset_info: features: - name: image dtype: image - name: annotation dtype: image splits: - name: train num_bytes: 1244980500.0 num_examples: 1187 - name: validation num_bytes: 311507338.0 num_examples: 297 download_size: 1162324382 dataset_size: 1556487838.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

heliosprime/twitter_dataset_1713022808

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 12086 num_examples: 28 download_size: 8949 dataset_size: 12086 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713022808" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_core-3__kuno-dogpark-7b

--- pretty_name: Evaluation run of core-3/kuno-dogpark-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [core-3/kuno-dogpark-7b](https://huggingface.co/core-3/kuno-dogpark-7b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_core-3__kuno-dogpark-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-03T22:15:40.975282](https://huggingface.co/datasets/open-llm-leaderboard/details_core-3__kuno-dogpark-7b/blob/main/results_2024-03-03T22-15-40.975282.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\": 0.6561091309021897,\n\ \ \"acc_stderr\": 0.03208260740047248,\n \"acc_norm\": 0.6557428188238892,\n\ \ \"acc_norm_stderr\": 0.032748123628975445,\n \"mc1\": 0.5569155446756426,\n\ \ \"mc1_stderr\": 0.017389730346877116,\n \"mc2\": 0.7113693226923901,\n\ \ \"mc2_stderr\": 0.014768500069791109\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6962457337883959,\n \"acc_stderr\": 0.013438909184778764,\n\ \ \"acc_norm\": 0.7184300341296929,\n \"acc_norm_stderr\": 0.013143376735009024\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.710017924716192,\n\ \ \"acc_stderr\": 0.004528264116475879,\n \"acc_norm\": 0.8814977096195977,\n\ \ \"acc_norm_stderr\": 0.0032254141192897155\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933713,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933713\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\ \ \"acc_stderr\": 0.03437079344106136,\n \"acc_norm\": 0.7847222222222222,\n\ \ \"acc_norm_stderr\": 0.03437079344106136\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878151,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878151\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083522,\n \"\ acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083522\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4827586206896552,\n \"acc_stderr\": 0.035158955511657,\n \"acc_norm\"\ : 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511657\n },\n\ \ \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\"\ : 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \ \ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857413,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857413\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.03006676158297793,\n \ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.03006676158297793\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659807,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659807\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\ : 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \"acc_norm\"\ : 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n },\n\ \ \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281365,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281365\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\ \ \"acc_stderr\": 0.013625556907993462,\n \"acc_norm\": 0.8237547892720306,\n\ \ \"acc_norm_stderr\": 0.013625556907993462\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.02370309952525818,\n\ \ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.02370309952525818\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4346368715083799,\n\ \ \"acc_stderr\": 0.01657899743549672,\n \"acc_norm\": 0.4346368715083799,\n\ \ \"acc_norm_stderr\": 0.01657899743549672\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.0242885336377261,\n\ \ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.0242885336377261\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.470013037809648,\n\ \ \"acc_stderr\": 0.012747248967079069,\n \"acc_norm\": 0.470013037809648,\n\ \ \"acc_norm_stderr\": 0.012747248967079069\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.02824568739146293,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.02824568739146293\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7510204081632653,\n \"acc_stderr\": 0.027682979522960234,\n\ \ \"acc_norm\": 0.7510204081632653,\n \"acc_norm_stderr\": 0.027682979522960234\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.02709729011807081,\n\ \ \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.02709729011807081\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5569155446756426,\n\ \ \"mc1_stderr\": 0.017389730346877116,\n \"mc2\": 0.7113693226923901,\n\ \ \"mc2_stderr\": 0.014768500069791109\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8224151539068666,\n \"acc_stderr\": 0.010740676861359238\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7050796057619408,\n \ \ \"acc_stderr\": 0.012560698010954767\n }\n}\n```" repo_url: https://huggingface.co/core-3/kuno-dogpark-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|arc:challenge|25_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-03T22-15-40.975282.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|gsm8k|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hellaswag|10_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-03T22-15-40.975282.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-management|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T22-15-40.975282.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|truthfulqa:mc|0_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-03T22-15-40.975282.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_03T22_15_40.975282 path: - '**/details_harness|winogrande|5_2024-03-03T22-15-40.975282.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-03T22-15-40.975282.parquet' - config_name: results data_files: - split: 2024_03_03T22_15_40.975282 path: - results_2024-03-03T22-15-40.975282.parquet - split: latest path: - results_2024-03-03T22-15-40.975282.parquet --- # Dataset Card for Evaluation run of core-3/kuno-dogpark-7b  Dataset automatically created during the evaluation run of model [core-3/kuno-dogpark-7b](https://huggingface.co/core-3/kuno-dogpark-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_core-3__kuno-dogpark-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-03T22:15:40.975282](https://huggingface.co/datasets/open-llm-leaderboard/details_core-3__kuno-dogpark-7b/blob/main/results_2024-03-03T22-15-40.975282.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": 0.6561091309021897, "acc_stderr": 0.03208260740047248, "acc_norm": 0.6557428188238892, "acc_norm_stderr": 0.032748123628975445, "mc1": 0.5569155446756426, "mc1_stderr": 0.017389730346877116, "mc2": 0.7113693226923901, "mc2_stderr": 0.014768500069791109 }, "harness|arc:challenge|25": { "acc": 0.6962457337883959, "acc_stderr": 0.013438909184778764, "acc_norm": 0.7184300341296929, "acc_norm_stderr": 0.013143376735009024 }, "harness|hellaswag|10": { "acc": 0.710017924716192, "acc_stderr": 0.004528264116475879, "acc_norm": 0.8814977096195977, "acc_norm_stderr": 0.0032254141192897155 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.02794321998933713, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933713 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106136, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106136 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878151, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878151 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406783, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406783 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083522, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083522 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511657, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511657 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.676923076923077, "acc_stderr": 0.02371088850197057, "acc_norm": 0.676923076923077, "acc_norm_stderr": 0.02371088850197057 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35555555555555557, "acc_stderr": 0.029185714949857413, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.029185714949857413 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6890756302521008, "acc_stderr": 0.03006676158297793, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.03006676158297793 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.39072847682119205, "acc_stderr": 0.03983798306659807, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.03983798306659807 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.0251956584289318, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.0251956584289318 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.035477710041594654, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.035477710041594654 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281365, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281365 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993462, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993462 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7369942196531792, "acc_stderr": 0.02370309952525818, "acc_norm": 0.7369942196531792, "acc_norm_stderr": 0.02370309952525818 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4346368715083799, "acc_stderr": 0.01657899743549672, "acc_norm": 0.4346368715083799, "acc_norm_stderr": 0.01657899743549672 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7438271604938271, "acc_stderr": 0.0242885336377261, "acc_norm": 0.7438271604938271, "acc_norm_stderr": 0.0242885336377261 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.470013037809648, "acc_stderr": 0.012747248967079069, "acc_norm": 0.470013037809648, "acc_norm_stderr": 0.012747248967079069 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.02824568739146293, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.02824568739146293 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7510204081632653, "acc_stderr": 0.027682979522960234, "acc_norm": 0.7510204081632653, "acc_norm_stderr": 0.027682979522960234 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.038786267710023595, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.038786267710023595 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8538011695906432, "acc_stderr": 0.02709729011807081, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.02709729011807081 }, "harness|truthfulqa:mc|0": { "mc1": 0.5569155446756426, "mc1_stderr": 0.017389730346877116, "mc2": 0.7113693226923901, "mc2_stderr": 0.014768500069791109 }, "harness|winogrande|5": { "acc": 0.8224151539068666, "acc_stderr": 0.010740676861359238 }, "harness|gsm8k|5": { "acc": 0.7050796057619408, "acc_stderr": 0.012560698010954767 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

youssef101/artelingo-dummy

--- license: mit dataset_info: features: - name: image dtype: image - name: art_style dtype: string - name: painting dtype: string - name: emotion dtype: string - name: language dtype: string - name: text dtype: string - name: split dtype: string splits: - name: train num_bytes: 18587167692.616 num_examples: 62989 - name: validation num_bytes: 965978050.797 num_examples: 3191 - name: test num_bytes: 2330046601.416 num_examples: 6402 download_size: 4565327615 dataset_size: 21883192344.829002 task_categories: - image-to-text - text-classification - image-classification - text-to-image - text-generation language: - en - ar - zh tags: - Affective Captioning - Emotions - Prediction - Art - ArtELingo pretty_name: ArtELingo size_categories: - 100K<n<1M --- ArtELingo is a benchmark and dataset introduced in a research paper aimed at promoting work on diversity across languages and cultures. It is an extension of ArtEmis, which is a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions. ArtELingo expands this dataset by adding 790,000 annotations in Arabic and Chinese. The purpose of these additional annotations is to evaluate the performance of "cultural-transfer" in AI systems. The dataset in ArtELingo contains many artworks with multiple annotations in three languages, providing a diverse set of data that enables the study of similarities and differences across languages and cultures. The researchers investigate captioning tasks and find that diversity in annotations improves the performance of baseline models. The goal of ArtELingo is to encourage research on multilinguality and culturally-aware AI. By including annotations in multiple languages and considering cultural differences, the dataset aims to build more human-compatible AI that is sensitive to emotional nuances across various cultural contexts. The researchers believe that studying emotions in this way is crucial to understanding a significant aspect of human intelligence. In summary, ArtELingo is a dataset that extends ArtEmis by providing annotations in multiple languages and cultures, facilitating research on diversity in AI systems and improving their performance in emotion-related tasks like label prediction and affective caption generation. The dataset is publicly available, and the researchers hope that it will facilitate future studies in multilingual and culturally-aware artificial intelligence.

kz919/databricks-dolly-15k

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 14275542 num_examples: 15011 download_size: 8073498 dataset_size: 14275542 license: apache-2.0 language: - en --- # Dataset Card for "databricks-dolly-15k" A instruction tuning formatted version of [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)

zh-tw-llm-dv/zh-tw-pythia-ta8000-v1-e1-tr_sg-301-c1024

--- dataset_info: dataset_size: 442704996.0 download_size: 177974029 features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - dtype: string name: preview splits: - name: train num_bytes: 441717119.0 num_examples: 306319 - name: test num_bytes: 987877.0 num_examples: 200 --- # zh-tw-pythia-ta8000-v1-e1-tr_sg-301-c1024 This dataset is a part of the `zh-tw-llm` project. * Tokenizer: `zh-tw-pythia-tokenizer-a8000-v1` * Built with: `translations`, `sharegpt` * Rows: `train` `306319`, `test` `200` * Max length: `1024` * Full config: ```json {"build_with": ["translations", "sharegpt"], "preview_length": 128, "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\nChinese: {lang_2}", "Chinese: {lang_2}\nEnglish: {lang_1}"], "use_template": "random", "rows_limit": 300000, "test_size": 100, "test_split_seed": 42}, "sharegpt_settings": {"source_dataset": "zetavg/ShareGPT-Processed", "train_on_inputs": false, "languages": [{"en": 0.4}, "zh_Hant"], "rows_limit": 8000, "test_size": 0.02, "test_split_seed": 42, "test_rows_limit": 100}} ```

autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574881

--- type: predictions tags: - autotrain - evaluation datasets: - xtreme eval_info: task: entity_extraction model: Cole/xlm-roberta-base-finetuned-panx-de metrics: [] dataset_name: xtreme dataset_config: PAN-X.de dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Cole/xlm-roberta-base-finetuned-panx-de * Dataset: xtreme To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

Codec-SUPERB/musdb18_synth

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 44100 - name: id dtype: string splits: - name: original num_bytes: 15593631670.0 num_examples: 750 - name: academicodec_hifi_16k_320d num_bytes: 5657371720.0 num_examples: 750 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 5657371720.0 num_examples: 750 - name: academicodec_hifi_24k_320d num_bytes: 8485912520.0 num_examples: 750 - name: dac_16k num_bytes: 5657596210.0 num_examples: 750 - name: dac_24k num_bytes: 8486361620.0 num_examples: 750 - name: dac_44k num_bytes: 15593634870.0 num_examples: 750 - name: encodec_24k num_bytes: 8486362626.0 num_examples: 750 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 5657538708.0 num_examples: 750 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 5657538314.0 num_examples: 750 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 5657538314.0 num_examples: 750 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 5657596210.0 num_examples: 750 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 5657596210.0 num_examples: 750 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 5657596210.0 num_examples: 750 - name: speech_tokenizer_16k num_bytes: 5657829320.0 num_examples: 750 download_size: 99080008677 dataset_size: 113221476242.0 configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k path: data/encodec_24k-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* ---

TurtleLiu/Psy_Test

--- license: apache-2.0 ---

Jorgvt/TID2013

--- tags: - image-quality pretty_name: TAMPERE IMAGE DATABASE 2013 size_categories: - 1K<n<10K ---

AI-Golden/aigolden-model

--- license: apache-2.0 ---

Minggz/Vi-Ner

--- task_categories: - token-classification language: - vi tags: - legal size_categories: - 10K<n<100K --- ## Vi-Ner ### Dataset Description ner_tags: a list of classification labels (int). Full tagset with indices: ```python {'B-DATETIME': 0, 'B-LOCATION': 1, 'B-ORGANIZATION': 2, 'B-PERSON': 3, 'I-DATETIME': 4, 'I-LOCATION': 5, 'I-ORGANIZATION': 6, 'I-PERSON': 7, 'O': 8} ``` ### Data Splits | name |train|validation|test| |---------|----:|---------:|---:| | Vi-Ner |19255| 2407|2407| ### Example An example of 'train' looks as follows. ``` { {'tokens': ['NSƯT', 'Hồng', 'Liên', '(trái)', 'đến', 'chúc', 'mừng', 'Thu', 'Trang..'], 'ner_tags': ['B-PERSON', 'I-PERSON', 'I-PERSON', 'O', 'O', 'O', 'O', 'B-PERSON', 'I-PERSON'], 'ner_idx': [3, 7, 7, 8, 8, 8, 8, 3, 7]} } ``` ### Usage ```python import datasets vi_ner = datasets.load_dataset('Minggz/Vi-Ner') vi_ner ```

tj-solergibert/SRV-Europarl-ST-processed-mt-de

--- dataset_info: features: - name: source_text dtype: string - name: dest_text dtype: string - name: dest_lang dtype: string splits: - name: train num_bytes: 142212387.7873306 num_examples: 570077 - name: valid num_bytes: 18480669.707563575 num_examples: 77255 - name: test num_bytes: 18441786.554772235 num_examples: 79827 download_size: 137284138 dataset_size: 179134844.0496664 --- # Dataset Card for "SRV-Europarl-ST-processed-mt-de" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nexdata/2418_Fight_Videos_Data

--- license: cc-by-nc-nd-4.0 --- ## Description 2,418 Fight Videos Data. The data includes indoor scenes (restaurant, arena, bus, boxing room, etc.) and outdoor scenes (road, crosswalk, lawn, etc.). The data covers multiple scenes, different time, multiple weather distribution. The data can be used for tasks such as fight behavior analysis. For more details, please refer to the link: https://www.nexdata.ai/dataset/1061?source=Huggingface # Specifications ## Data size 2,418 videos, including 2,285 fight videos of humans, 5 fight videos of humans and animals, and 128 fight videos of animals ## Collecting environment including indoor scenes (restaurant, arena, bus, boxing room, etc.), outdoor scenes (road, crosswalk, lawn, etc.) ## Diversity multiple scenes, different time, multiple weather distribution ## Divece including camera (very few videos) and cellphone ## Collecting time day, night ## Image Parameter the video data format is .mp4 # Licensing Information Commercial License

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/39661990

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1339 dataset_size: 180 --- # Dataset Card for "39661990" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dputilov/midascontrolourbalanced

--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: mask dtype: image - name: text dtype: string splits: - name: train num_bytes: 1537158001.974634 num_examples: 2725 download_size: 1526594996 dataset_size: 1537158001.974634 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "midascontrolourbalanced" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

wenhanhan/PUBHEALTH_train

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 46953437 num_examples: 9804 download_size: 26528789 dataset_size: 46953437 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "PUBHEALTH_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

WelfCrozzo/kupalinka-sum

--- dataset_info: features: - name: x dtype: string - name: y dtype: string - name: lang dtype: string splits: - name: train num_bytes: 839341049 num_examples: 332876 - name: validation num_bytes: 93823377 num_examples: 37057 download_size: 516244839 dataset_size: 933164426 --- # Dataset Card for "kupalinka-sum" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sasha/stablediffusionbias

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

TenzinGayche/tts_nocs_b3

--- dataset_info: features: - name: file_name dtype: audio: sampling_rate: 16000 - name: uni dtype: string - name: wylie dtype: string - name: url dtype: string - name: dept dtype: string - name: timestamp dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5624702984.046 num_examples: 92167 download_size: 27280617879 dataset_size: 5624702984.046 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "tts_nocs_b3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

megantron/labeled_aesthetics_simpsons

--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 splits: - name: train num_bytes: 69240975.0 num_examples: 178 download_size: 69245463 dataset_size: 69240975.0 --- # Dataset Card for "labeled_aesthetics_simpsons" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

natnitaract/teetouchjaknamon-faissbatchall-index-2

--- license: cc-by-3.0 ---

irds/wikiclir_de

--- pretty_name: '`wikiclir/de`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `wikiclir/de` The `wikiclir/de` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/de). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=2,091,278 - `queries` (i.e., topics); count=938,217 - `qrels`: (relevance assessments); count=5,550,454 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/wikiclir_de', 'docs') for record in docs: record # {'doc_id': ..., 'title': ..., 'text': ...} queries = load_dataset('irds/wikiclir_de', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/wikiclir_de', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{sasaki-etal-2018-cross, title = "Cross-Lingual Learning-to-Rank with Shared Representations", author = "Sasaki, Shota and Sun, Shuo and Schamoni, Shigehiko and Duh, Kevin and Inui, Kentaro", booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N18-2073", doi = "10.18653/v1/N18-2073", pages = "458--463" } ```

joey234/mmlu-high_school_mathematics-rule-neg

--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 55920 num_examples: 270 download_size: 33612 dataset_size: 55920 --- # Dataset Card for "mmlu-high_school_mathematics-rule-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AdapterOcean/med_alpaca_standardized_cluster_98_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 13488443 num_examples: 7117 download_size: 7232687 dataset_size: 13488443 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_98_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dmargutierrez/Babelscape-wikineural-joined

--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: int64 - name: lang dtype: string splits: - name: train num_bytes: 319328641 num_examples: 821600 - name: validation num_bytes: 39434957 num_examples: 102700 - name: test num_bytes: 39371980 num_examples: 103206 download_size: 139847318 dataset_size: 398135578 task_categories: - token-classification language: - es - en - nl - fr - it - ru - pt - pl - de pretty_name: Wikineural tags: - named-entity-recognition - wikipedia - machine-generation --- # Dataset Card for "Babelscape-wikineural-joined" This dataset is a merged version of [wikineural](https://huggingface.co/datasets/Babelscape/wikineural) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) <pre><code> @inproceedings{tedeschi-etal-2021-wikineural-combined, title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", author = "Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\`o} and Cecconi, Francesco and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.215", pages = "2521--2533", abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", } </code></pre>

gigant/tib_2303_filtered

--- dataset_info: features: - name: doi dtype: string - name: title dtype: string - name: url dtype: string - name: video_url dtype: string - name: license dtype: string - name: subject dtype: string - name: genre dtype: string - name: release_year dtype: string - name: author dtype: string - name: contributors dtype: string - name: abstract dtype: string - name: transcript dtype: string - name: transcript_segments sequence: - name: id dtype: int32 - name: seek dtype: int32 - name: start dtype: float32 - name: end dtype: float32 - name: text dtype: string - name: tokens sequence: int32 - name: temperature dtype: float32 - name: avg_logprob dtype: float32 - name: compression_ratio dtype: float32 - name: no_speech_prob dtype: float32 - name: keyframes sequence: - name: slide dtype: string - name: frames sequence: int32 - name: timestamp sequence: float32 - name: language dtype: string splits: - name: train num_bytes: 1032202393.542393 num_examples: 9103 download_size: 500663533 dataset_size: 1032202393.542393 --- # Dataset Card for "tib_2303_filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sloango/github-issues

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: labels list: - name: id dtype: int64 - name: node_id dtype: string - name: url dtype: string - name: name dtype: string - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: assignees list: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: comments sequence: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: body dtype: string - name: reactions struct: - name: url dtype: string - name: total_count dtype: int64 - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: laugh dtype: int64 - name: hooray dtype: int64 - name: confused dtype: int64 - name: heart dtype: int64 - name: rocket dtype: int64 - name: eyes dtype: int64 - name: timeline_url dtype: string - name: state_reason dtype: string - name: draft dtype: bool - name: pull_request struct: - name: url dtype: string - name: html_url dtype: string - name: diff_url dtype: string - name: patch_url dtype: string - name: merged_at dtype: timestamp[s] - name: is_pull_request dtype: bool splits: - name: train num_bytes: 30030287 num_examples: 5000 download_size: 8781855 dataset_size: 30030287 --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mvkvc/artifact-10k

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ai '1': real splits: - name: train num_bytes: 99148840.0 num_examples: 8000 - name: test num_bytes: 25474768.0 num_examples: 2000 download_size: 125994503 dataset_size: 124623608.0 --- # Dataset Card for "critique-10k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MaxYuki/Izuku_Midoriya

--- license: apache-2.0 ---

Back-up/chung-khoan-demo-p7

--- dataset_info: features: - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: view struct: - name: number_of_response dtype: string - name: number_of_view dtype: string - name: content list: - name: res dtype: string splits: - name: train num_bytes: 44370120 num_examples: 8859 download_size: 15521580 dataset_size: 44370120 configs: - config_name: default data_files: - split: train path: data/train-* ---

Luciya/llama-2-nuv-intent-noE-oos

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 828135 num_examples: 1834 download_size: 127293 dataset_size: 828135 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "llama-2-nuv-intent-noE-oos" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zooxsmartufpb/dataset_complete3

--- dataset_info: features: - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 81060969 num_examples: 46099 download_size: 8042824 dataset_size: 81060969 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dataset_complete3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

PleIAs/pd_authors_openlibrary

Invalid username or password.

NebulaByte/E-Commerce_FAQs

--- license: apache-2.0 ---

Deysi/spam-detection-dataset

--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 3161821 num_examples: 8175 - name: test num_bytes: 1094757 num_examples: 2725 download_size: 2578551 dataset_size: 4256578 license: apache-2.0 task_categories: - text-classification language: - en pretty_name: spam size_categories: - 10K<n<100K --- # Dataset Card for "spam-detection-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hpprc/jagovfaqs

--- dataset_info: - config_name: collection features: - name: text dtype: string splits: - name: train num_bytes: 13918890 num_examples: 22794 download_size: 5874592 dataset_size: 13918890 - config_name: dataset features: - name: anc dtype: string - name: pos_ids sequence: int64 - name: neg_ids sequence: 'null' splits: - name: train num_bytes: 4469001 num_examples: 22794 download_size: 1924819 dataset_size: 4469001 configs: - config_name: collection data_files: - split: train path: collection/train-* - config_name: dataset data_files: - split: train path: dataset/train-* ---

TrainingDataPro/monitors-replay-attacks-dataset

--- license: cc-by-nc-nd-4.0 task_categories: - video-classification language: - en tags: - legal dataset_info: features: - name: file dtype: string - name: phone dtype: string - name: computer dtype: string - name: gender dtype: string - name: age dtype: int16 - name: country dtype: string splits: - name: train num_bytes: 588 num_examples: 10 download_size: 342902185 dataset_size: 588 --- # Monitors Replay Attacks Dataset The dataset consists of videos of replay attacks played on different models of computers. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems. The dataset includes: **replay attacks** - videos of real people played on a computer and filmed on the phone. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2Fa40451e66953bd1652887400c0eae4be%2FUntitled.png?generation=1688049829507934&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/anti-spoofing-replay?utm_source=huggingface&utm_medium=cpc&utm_campaign=monitors-replay-attacks-dataset) to discuss your requirements, learn about the price and buy the dataset. # Content The folder "attacks" includes videos of replay attacks ### Computer companies in the datset: - Dell - LG - ASUS - HP - Redmi - AOC - Samsung ### File with the extension .csv includes the following information for each media file: - **file**: link to access the replay video, - **phone**: the device used to capture the replay video, - **computer**: the device used to play the video, - **gender**: gender of a person in the video, - **age**: age of the person in the video, - **country**: country of the person ## [**TrainingData**](https://trainingdata.pro/data-market/anti-spoofing-replay?utm_source=huggingface&utm_medium=cpc&utm_campaign=monitors-replay-attacks-dataset) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**

mshenoda/spam-messages

--- license: mit --- ## Dataset The dataset is composed of messages labeled by ham or spam, merged from three data sources: - SMS Spam Collection https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset - Telegram Spam Ham https://huggingface.co/datasets/thehamkercat/telegram-spam-ham/tree/main - Enron Spam: https://huggingface.co/datasets/SetFit/enron_spam/tree/main (only used message column and labels) The prepare script for enron is available at https://github.com/mshenoda/roberta-spam/tree/main/data/enron. The data is split 80% train 10% validation, and 10% test sets; the scripts used to split and merge of the three data sources are available at: https://github.com/mshenoda/roberta-spam/tree/main/data/utils. ### Dataset Class Distribution Training 80% | Validation 10% | Testing 10% :-------------------------:|:-------------------------:|:-------------------------: ![](plots/train_set_distribution.jpg "Train Distribution") Class Distribution | ![](plots/val_set_distribution.jpg "Validation Distribution") Class Distribution | ![](plots/test_set_distribution.jpg "Test Distribution") Class Distribution

Andregomes/nlpC

--- license: bigscience-openrail-m ---

3mrys/daset_2

--- license: apache-2.0 ---

dalow24/testing

--- license: afl-3.0 ---

stevied67/autotrain-data-pegasus-reddit-summarizer

--- language: - en task_categories: - summarization --- # AutoTrain Dataset for project: pegasus-reddit-summarizer ## Dataset Description This dataset has been automatically processed by AutoTrain for project pegasus-reddit-summarizer. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_id": "82n2za", "text": "User who has been working in sales for 30+ years gets a new laptop on Monday. This morning when I get in, my phone is ringing already. I'm not supposed to start for another 20 mins, but I'm nice, so I answer it.\n\n\"This new laptop doesn't have Microsoft on it. Do I need to bring it back in? Just I'm in Scotland, so I'll have to fly down again.\"\n\nEr, yes it does. We went through it when I handed it over, I showed you Outlook, and how Outlook 2016 looks ever so slightly different to Outlook 2010 on your old laptop.\n\n\"Look, it's not there. Every time I click on the button, it just opens the internet. I've emailed my boss from my phone to let him know I'm cancelling all my appointments today, so can you fix it over the VPN or do I need to fly down?\"\n\nSo, I ask him what he's clicking on. \"The blue E. You said the icon was blue now instead of orange. But that just opens the internet, I've already TOLD YOU.\"\n\nI ask him to look along the taskbar for any other blue icons. \"There's a blue and white O. Are you telling me that's it?\" I ask him to confirm that Outlook begins with the letter O, and advise him to try clicking on that icon instead.\n\nSo he clicks on it, and ta-da! Outlook opens. \"Oh for God's sake. This is too confusing. Why did you change the colour anyway? Now I have to re-arrange all my appointments, this is really inconvenient.\"\n\nSorry, I did ring up my mate Bill and ask him to change the colour of Outlook from orange to blue just to confuse you. Luckily I have great power and influence over at Microsoft, so they did me a favour, and I'm now reaping the untold rewards.\n\nGTG, writing an email to his boss to cover my arse...\n", "target": "User receives a new laptop and complains to IT that it doesn't have Microsoft on it. IT informs the user that they had gone through it when handing it over and that the user had simply clicked on the wrong icon. The user complains about the change in icon color and that they now have to rearrange their entire schedule. IT sarcastically apologizes and writes an email to cover themselves." }, { "feat_id": "q4kjoe", "text": "The title implies I was there but really it was just my mom and my sister.\n\nMy sister was craving a cheddar jalapeo bagel so my mom decided to go to a chain caf to get one for her. It was 10 minutes before closing, and they went through the drive thru. My mom orders the cheddar bagel for my sister plus some other things for the rest of the people at home, including coffee cake. The gal at the drive thru window said \"you're lucky, you're getting the last ones of everything you're ordering!\"\n\nMy mom pulls up to the window to pay and receive the food and the drive thru gal (about 19) is crying and apologizing profusely. She says the people in front of my mom STOLE THE FOOD. Mom asked how it happened and the lady said that she had made a mistake and was about to give the car in front the wrong order, but she realized her mistake before handing it over and announced it. The people then REACHED for the bag (it was not handed to them!!!) and stole it, apparently saying \"you can't have it back now, it's cross contaminated!\" Then when the lady called for her manager, he was busy, and the people's order wasn't ready yet, so the poor gal just told them to pull up and wait for their food and they did.\n\nMy mom is a really loving person and so she's trying to tell this lady it's okay, she didn't really need the food, she's not mad, etc., and in the meantime the manager comes over to ask what is happening. She tells him and he is shocked. He asked if the car in front was those people, and she said yes. So he starts going out to talk to the people in the car, and at that moment, they step on it and zip out of the parking lot. \n\nSo now those people have not only stolen my mom's order, which were the last items, but they didn't even receive their order! But the good news is that the manager said to my mom that he had been saving a cheddar bagel for himself and that he would give that one to her free of charge. \n\nHave you ever heard of anything like this??? My mom told me this on the phone and I was stunned. I've worked food service before but nothing like this has ever happened!! She thinks the people in the other car had done this maneuver before since the \"cross contamination\" response came out way too quickly. Also I feel so sorry for the lady! She's working in a fucking pandemic getting underpaid and overworked and now has to deal with deranged people!", "target": "A woman went to a chain caf\u00e9 with her daughter to buy a cheddar jalape\u00f1o bagel for her sister. The drive thru attendant announces they are getting the last items of everything. The attendant then reveals that the people in the car in front of them stole their food. The woman's mother attempted to comfort the attendant and the manager offered the woman a cheddar bagel for free. The woman wonders if the \"cross contamination\" defense may have been used by the thieves before." } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_id": "Value(dtype='string', id=None)", "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 7200 | | valid | 1800 |

tyzhu/squad_qa_wrong_num_v5_full_recite_full_passage_random_permute_rerun_1

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 5171543.277877698 num_examples: 2875 - name: validation num_bytes: 584108 num_examples: 300 download_size: 1519861 dataset_size: 5755651.277877698 --- # Dataset Card for "squad_qa_wrong_num_v5_full_recite_full_passage_random_permute_rerun_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

UnderstandLing/oasst1_bn

--- license: apache-2.0 dataset_info: features: - name: message_id dtype: string - name: parent_id dtype: string - name: user_id dtype: string - name: created_date dtype: string - name: text dtype: string - name: role dtype: string - name: lang dtype: string - name: review_count dtype: int64 - name: review_result dtype: bool - name: deleted dtype: bool - name: rank dtype: float64 - name: synthetic dtype: bool - name: model_name dtype: 'null' - name: detoxify struct: - name: identity_attack dtype: float64 - name: insult dtype: float64 - name: obscene dtype: float64 - name: severe_toxicity dtype: float64 - name: sexual_explicit dtype: float64 - name: threat dtype: float64 - name: toxicity dtype: float64 - name: message_tree_id dtype: string - name: tree_state dtype: string - name: emojis struct: - name: count sequence: int64 - name: name sequence: string - name: labels struct: - name: count sequence: int64 - name: name sequence: string - name: value sequence: float64 splits: - name: train num_bytes: 117761745 num_examples: 83078 - name: validation num_bytes: 5686930 num_examples: 3946 download_size: 31092337 dataset_size: 123448675 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

dwadden/healthver_entailment

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking pretty_name: HealthVer dataset_info: features: - name: claim_id dtype: int32 - name: claim dtype: string - name: abstract_id dtype: int32 - name: title dtype: string - name: abstract sequence: string - name: verdict dtype: string - name: evidence sequence: int32 splits: - name: train num_bytes: 9490482 num_examples: 5292 - name: validation num_bytes: 1707997 num_examples: 940 - name: test num_bytes: 1620257 num_examples: 903 download_size: 3610222 dataset_size: 12818736 --- # Dataset Card for "healthver_entailment" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) ## Dataset Description - **Repository:** <https://github.com/sarrouti/HealthVe> - **Point of Contact:** [David Wadden](mailto:davidw@allenai.org) ### Dataset Summary HealthVer is a dataset of public health claims, verified against scientific research articles. For this version of the dataset, we follow the preprocessing from the MultiVerS modeling paper https://github.com/dwadden/multivers, verifying claims against full article abstracts rather than individual sentences. Entailment labels and rationales are included. ## Dataset Structure ### Data fields - `claim_id`: An `int32` claim identifier. - `claim`: A `string`. - `abstract_id`: An `int32` abstract identifier. - `title`: A `string`. - `abstract`: A list of `strings`, one for each sentence in the abstract. - `verdict`: The fact-checking verdict, a `string`. - `evidence`: A list of sentences from the abstract which provide evidence for the verdict.

lingtrain/sanskrit-russian

--- dataset_info: features: - name: ru dtype: string - name: san dtype: string splits: - name: train num_bytes: 13189111 num_examples: 32669 download_size: 7022744 dataset_size: 13189111 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sanskrit-russian" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

heliosprime/twitter_dataset_1712952491

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 14426 num_examples: 33 download_size: 10088 dataset_size: 14426 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1712952491" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)