librarian-bots/dataset_cards_with_metadata

datasetId stringlengths 5 121	author stringlengths 2 42	last_modified unknown	downloads int64 0 29.6M	likes int64 0 5.88k	tags sequencelengths 1 7.92k	task_categories sequencelengths 0 40 ⌀	createdAt unknown	card stringlengths 19 977k
hails/mmlu_no_train	hails	"2024-01-22T20:46:30"	29,594,679	24	[ "task_categories:question-answering", "language:en", "license:mit", "region:us" ]	[ "question-answering" ]	"2023-10-31T17:25:54"	--- language: - en license: mit task_categories: - question-answering pretty_name: MMLU loader with no auxiliary train set dataset_info: config_name: all features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 6967453 num_examples: 14042 - name: validation num_bytes: 763484 num_examples: 1531 - name: dev num_bytes: 125353 num_examples: 285 download_size: 3987384 dataset_size: 7856290 configs: - config_name: all data_files: - split: test path: all/test-* - split: validation path: all/validation-* - split: dev path: all/dev-* --- This dataset contains a copy of the `cais/mmlu` HF dataset but without the `auxiliary_train` split that takes a long time to generate again each time when loading multiple subsets of the dataset. Please visit https://huggingface.co/datasets/cais/mmlu for more information on the MMLU dataset.
lighteval/mmlu	lighteval	"2023-06-09T16:36:19"	3,665,175	35	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:1M<n<10M", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2009.03300", "arxiv:2005.00700", "arxiv:2005.14165", "arxiv:2008.02275", "region:us" ]	[ "question-answering" ]	"2023-05-16T09:39:28"	--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: mmlu pretty_name: Measuring Massive Multitask Language Understanding language_bcp47: - en-US dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 19328 num_examples: 100 - name: validation num_bytes: 2024 num_examples: 11 - name: dev num_bytes: 830 num_examples: 5 download_size: 166184960 dataset_size: 160623559 - config_name: anatomy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33121 num_examples: 135 - name: validation num_bytes: 3140 num_examples: 14 - name: dev num_bytes: 967 num_examples: 5 download_size: 166184960 dataset_size: 160638605 - config_name: astronomy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46771 num_examples: 152 - name: validation num_bytes: 5027 num_examples: 16 - name: dev num_bytes: 2076 num_examples: 5 download_size: 166184960 dataset_size: 160655251 - config_name: business_ethics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33252 num_examples: 100 - name: validation num_bytes: 3038 num_examples: 11 - name: dev num_bytes: 2190 num_examples: 5 download_size: 166184960 dataset_size: 160639857 - config_name: clinical_knowledge features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 62754 num_examples: 265 - name: validation num_bytes: 6664 num_examples: 29 - name: dev num_bytes: 1210 num_examples: 5 download_size: 166184960 dataset_size: 160672005 - config_name: college_biology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 48797 num_examples: 144 - name: validation num_bytes: 4819 num_examples: 16 - name: dev num_bytes: 1532 num_examples: 5 download_size: 166184960 dataset_size: 160656525 - config_name: college_chemistry features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 24708 num_examples: 100 - name: validation num_bytes: 2328 num_examples: 8 - name: dev num_bytes: 1331 num_examples: 5 download_size: 166184960 dataset_size: 160629744 - config_name: college_computer_science features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 42641 num_examples: 100 - name: validation num_bytes: 4663 num_examples: 11 - name: dev num_bytes: 2765 num_examples: 5 download_size: 166184960 dataset_size: 160651446 - config_name: college_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 24711 num_examples: 100 - name: validation num_bytes: 2668 num_examples: 11 - name: dev num_bytes: 1493 num_examples: 5 download_size: 166184960 dataset_size: 160630249 - config_name: college_medicine features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 82397 num_examples: 173 - name: validation num_bytes: 7909 num_examples: 22 - name: dev num_bytes: 1670 num_examples: 5 download_size: 166184960 dataset_size: 160693353 - config_name: college_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 30181 num_examples: 102 - name: validation num_bytes: 3490 num_examples: 11 - name: dev num_bytes: 1412 num_examples: 5 download_size: 166184960 dataset_size: 160636460 - config_name: computer_security features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 27124 num_examples: 100 - name: validation num_bytes: 4549 num_examples: 11 - name: dev num_bytes: 1101 num_examples: 5 download_size: 166184960 dataset_size: 160634151 - config_name: conceptual_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 40709 num_examples: 235 - name: validation num_bytes: 4474 num_examples: 26 - name: dev num_bytes: 934 num_examples: 5 download_size: 166184960 dataset_size: 160647494 - config_name: econometrics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46547 num_examples: 114 - name: validation num_bytes: 4967 num_examples: 12 - name: dev num_bytes: 1644 num_examples: 5 download_size: 166184960 dataset_size: 160654535 - config_name: electrical_engineering features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 25142 num_examples: 145 - name: validation num_bytes: 2903 num_examples: 16 - name: dev num_bytes: 972 num_examples: 5 download_size: 166184960 dataset_size: 160630394 - config_name: elementary_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 70108 num_examples: 378 - name: validation num_bytes: 8988 num_examples: 41 - name: dev num_bytes: 1440 num_examples: 5 download_size: 166184960 dataset_size: 160681913 - config_name: formal_logic features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 49785 num_examples: 126 - name: validation num_bytes: 6252 num_examples: 14 - name: dev num_bytes: 1757 num_examples: 5 download_size: 166184960 dataset_size: 160659171 - config_name: global_facts features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 18403 num_examples: 100 - name: validation num_bytes: 1865 num_examples: 10 - name: dev num_bytes: 1229 num_examples: 5 download_size: 166184960 dataset_size: 160622874 - config_name: high_school_biology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 109732 num_examples: 310 - name: validation num_bytes: 11022 num_examples: 32 - name: dev num_bytes: 1673 num_examples: 5 download_size: 166184960 dataset_size: 160723804 - config_name: high_school_chemistry features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 58464 num_examples: 203 - name: validation num_bytes: 7092 num_examples: 22 - name: dev num_bytes: 1220 num_examples: 5 download_size: 166184960 dataset_size: 160668153 - config_name: high_school_computer_science features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 44476 num_examples: 100 - name: validation num_bytes: 3343 num_examples: 9 - name: dev num_bytes: 2918 num_examples: 5 download_size: 166184960 dataset_size: 160652114 - config_name: high_school_european_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 270300 num_examples: 165 - name: validation num_bytes: 29632 num_examples: 18 - name: dev num_bytes: 11564 num_examples: 5 download_size: 166184960 dataset_size: 160912873 - config_name: high_school_geography features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 42034 num_examples: 198 - name: validation num_bytes: 4332 num_examples: 22 - name: dev num_bytes: 1403 num_examples: 5 download_size: 166184960 dataset_size: 160649146 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 66074 num_examples: 193 - name: validation num_bytes: 7063 num_examples: 21 - name: dev num_bytes: 1779 num_examples: 5 download_size: 166184960 dataset_size: 160676293 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 117687 num_examples: 390 - name: validation num_bytes: 13020 num_examples: 43 - name: dev num_bytes: 1328 num_examples: 5 download_size: 166184960 dataset_size: 160733412 - config_name: high_school_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 54854 num_examples: 270 - name: validation num_bytes: 5765 num_examples: 29 - name: dev num_bytes: 1297 num_examples: 5 download_size: 166184960 dataset_size: 160663293 - config_name: high_school_microeconomics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 75703 num_examples: 238 - name: validation num_bytes: 7553 num_examples: 26 - name: dev num_bytes: 1298 num_examples: 5 download_size: 166184960 dataset_size: 160685931 - config_name: high_school_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 59538 num_examples: 151 - name: validation num_bytes: 6771 num_examples: 17 - name: dev num_bytes: 1489 num_examples: 5 download_size: 166184960 dataset_size: 160669175 - config_name: high_school_psychology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 159407 num_examples: 545 - name: validation num_bytes: 17269 num_examples: 60 - name: dev num_bytes: 1905 num_examples: 5 download_size: 166184960 dataset_size: 160779958 - config_name: high_school_statistics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 110702 num_examples: 216 - name: validation num_bytes: 9997 num_examples: 23 - name: dev num_bytes: 2528 num_examples: 5 download_size: 166184960 dataset_size: 160724604 - config_name: high_school_us_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 296734 num_examples: 204 - name: validation num_bytes: 31706 num_examples: 22 - name: dev num_bytes: 8864 num_examples: 5 download_size: 166184960 dataset_size: 160938681 - config_name: high_school_world_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 378617 num_examples: 237 - name: validation num_bytes: 45501 num_examples: 26 - name: dev num_bytes: 4882 num_examples: 5 download_size: 166184960 dataset_size: 161030377 - config_name: human_aging features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46098 num_examples: 223 - name: validation num_bytes: 4707 num_examples: 23 - name: dev num_bytes: 1008 num_examples: 5 download_size: 166184960 dataset_size: 160653190 - config_name: human_sexuality features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 32110 num_examples: 131 - name: validation num_bytes: 2421 num_examples: 12 - name: dev num_bytes: 1077 num_examples: 5 download_size: 166184960 dataset_size: 160636985 - config_name: international_law features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 53531 num_examples: 121 - name: validation num_bytes: 6473 num_examples: 13 - name: dev num_bytes: 2418 num_examples: 5 download_size: 166184960 dataset_size: 160663799 - config_name: jurisprudence features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33986 num_examples: 108 - name: validation num_bytes: 3729 num_examples: 11 - name: dev num_bytes: 1303 num_examples: 5 download_size: 166184960 dataset_size: 160640395 - config_name: logical_fallacies features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 50117 num_examples: 163 - name: validation num_bytes: 5103 num_examples: 18 - name: dev num_bytes: 1573 num_examples: 5 download_size: 166184960 dataset_size: 160658170 - config_name: machine_learning features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33880 num_examples: 112 - name: validation num_bytes: 3232 num_examples: 11 - name: dev num_bytes: 2323 num_examples: 5 download_size: 166184960 dataset_size: 160640812 - config_name: management features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 20002 num_examples: 103 - name: validation num_bytes: 1820 num_examples: 11 - name: dev num_bytes: 898 num_examples: 5 download_size: 166184960 dataset_size: 160624097 - config_name: marketing features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 63025 num_examples: 234 - name: validation num_bytes: 7394 num_examples: 25 - name: dev num_bytes: 1481 num_examples: 5 download_size: 166184960 dataset_size: 160673277 - config_name: medical_genetics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 20864 num_examples: 100 - name: validation num_bytes: 3005 num_examples: 11 - name: dev num_bytes: 1089 num_examples: 5 download_size: 166184960 dataset_size: 160626335 - config_name: miscellaneous features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 147704 num_examples: 783 - name: validation num_bytes: 14330 num_examples: 86 - name: dev num_bytes: 699 num_examples: 5 download_size: 166184960 dataset_size: 160764110 - config_name: moral_disputes features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 107818 num_examples: 346 - name: validation num_bytes: 12420 num_examples: 38 - name: dev num_bytes: 1755 num_examples: 5 download_size: 166184960 dataset_size: 160723370 - config_name: moral_scenarios features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 374026 num_examples: 895 - name: validation num_bytes: 42338 num_examples: 100 - name: dev num_bytes: 2058 num_examples: 5 download_size: 166184960 dataset_size: 161019799 - config_name: nutrition features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 92410 num_examples: 306 - name: validation num_bytes: 8436 num_examples: 33 - name: dev num_bytes: 2085 num_examples: 5 download_size: 166184960 dataset_size: 160704308 - config_name: philosophy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 80073 num_examples: 311 - name: validation num_bytes: 9184 num_examples: 34 - name: dev num_bytes: 988 num_examples: 5 download_size: 166184960 dataset_size: 160691622 - config_name: prehistory features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 89594 num_examples: 324 - name: validation num_bytes: 10285 num_examples: 35 - name: dev num_bytes: 1878 num_examples: 5 download_size: 166184960 dataset_size: 160703134 - config_name: professional_accounting features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 124550 num_examples: 282 - name: validation num_bytes: 14372 num_examples: 31 - name: dev num_bytes: 2148 num_examples: 5 download_size: 166184960 dataset_size: 160742447 - config_name: professional_law features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 1891762 num_examples: 1534 - name: validation num_bytes: 203519 num_examples: 170 - name: dev num_bytes: 6610 num_examples: 5 download_size: 166184960 dataset_size: 162703268 - config_name: professional_medicine features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 217561 num_examples: 272 - name: validation num_bytes: 23847 num_examples: 31 - name: dev num_bytes: 3807 num_examples: 5 download_size: 166184960 dataset_size: 160846592 - config_name: professional_psychology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 225899 num_examples: 612 - name: validation num_bytes: 29101 num_examples: 69 - name: dev num_bytes: 2267 num_examples: 5 download_size: 166184960 dataset_size: 160858644 - config_name: public_relations features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 28760 num_examples: 110 - name: validation num_bytes: 4566 num_examples: 12 - name: dev num_bytes: 1496 num_examples: 5 download_size: 166184960 dataset_size: 160636199 - config_name: security_studies features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 204844 num_examples: 245 - name: validation num_bytes: 22637 num_examples: 27 - name: dev num_bytes: 5335 num_examples: 5 download_size: 166184960 dataset_size: 160834193 - config_name: sociology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 66243 num_examples: 201 - name: validation num_bytes: 7184 num_examples: 22 - name: dev num_bytes: 1613 num_examples: 5 download_size: 166184960 dataset_size: 160676417 - config_name: us_foreign_policy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 28443 num_examples: 100 - name: validation num_bytes: 3264 num_examples: 11 - name: dev num_bytes: 1611 num_examples: 5 download_size: 166184960 dataset_size: 160634695 - config_name: virology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 38759 num_examples: 166 - name: validation num_bytes: 5463 num_examples: 18 - name: dev num_bytes: 1096 num_examples: 5 download_size: 166184960 dataset_size: 160646695 - config_name: world_religions features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 25274 num_examples: 171 - name: validation num_bytes: 2765 num_examples: 19 - name: dev num_bytes: 670 num_examples: 5 download_size: 166184960 dataset_size: 160630086 --- # Dataset Card for MMLU ## 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 - Repository: https://github.com/hendrycks/test - Paper: https://arxiv.org/abs/2009.03300 ### Dataset Summary [Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions'] ### Supported Tasks and Leaderboards \| Model \| Authors \| Humanities \| Social Science \| STEM \| Other \| Average \| \|------------------------------------\|----------\|:-------:\|:-------:\|:-------:\|:-------:\|:-------:\| \| [UnifiedQA](https://arxiv.org/abs/2005.00700) \| Khashabi et al., 2020 \| 45.6 \| 56.6 \| 40.2 \| 54.6 \| 48.9 \| [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) \| Brown et al., 2020 \| 40.8 \| 50.4 \| 36.7 \| 48.8 \| 43.9 \| [GPT-2](https://arxiv.org/abs/2005.14165) \| Radford et al., 2019 \| 32.8 \| 33.3 \| 30.2 \| 33.1 \| 32.4 \| Random Baseline \| N/A \| 25.0 \| 25.0 \| 25.0 \| 25.0 \| 25.0 \| 25.0 ### Languages English ## Dataset Structure ### Data Instances An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"], "answer": "D" } ``` ### Data Fields - `question`: a string feature - `choices`: a list of 4 string features - `answer`: a ClassLabel feature ### Data Splits - `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc. - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask \| \| auxiliary_train \| dev \| val \| test \| \| ----- \| :------: \| :-----: \| :-----: \| :-----: \| \| TOTAL \| 99842 \| 285 \| 1531 \| 14042 ## Dataset Creation ### Curation Rationale Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn. ### 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 [MIT License](https://github.com/hendrycks/test/blob/master/LICENSE) ### Citation Information If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from: ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ``` ### Contributions Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
SaylorTwift/bbh	SaylorTwift	"2024-06-16T12:12:34"	2,589,462	2	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	null	"2024-06-12T15:26:17"	--- dataset_info: - config_name: boolean_expressions features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 11790 num_examples: 250 download_size: 4700 dataset_size: 11790 - config_name: causal_judgement features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 198021 num_examples: 187 download_size: 69494 dataset_size: 198021 - config_name: date_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 54666 num_examples: 250 download_size: 18041 dataset_size: 54666 - config_name: default features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 50971 num_examples: 250 download_size: 21723 dataset_size: 50971 - config_name: disambiguation_qa features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 78620 num_examples: 250 download_size: 16704 dataset_size: 78620 - config_name: dyck_languages features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38432 num_examples: 250 download_size: 10015 dataset_size: 38432 - config_name: formal_fallacies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 138224 num_examples: 250 download_size: 35789 dataset_size: 138224 - config_name: geometric_shapes features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 68560 num_examples: 250 download_size: 20233 dataset_size: 68560 - config_name: hyperbaton features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38574 num_examples: 250 download_size: 10422 dataset_size: 38574 - config_name: logical_deduction_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 148595 num_examples: 250 download_size: 33498 dataset_size: 148595 - config_name: logical_deduction_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 191022 num_examples: 250 download_size: 43970 dataset_size: 191022 - config_name: logical_deduction_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 105831 num_examples: 250 download_size: 21597 dataset_size: 105831 - config_name: movie_recommendation features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 50971 num_examples: 250 download_size: 21723 dataset_size: 50971 - config_name: multistep_arithmetic_two features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 12943 num_examples: 250 download_size: 7552 dataset_size: 12943 - config_name: navigate features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 49031 num_examples: 250 download_size: 10032 dataset_size: 49031 - config_name: object_counting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 30508 num_examples: 250 download_size: 10586 dataset_size: 30508 - config_name: penguins_in_a_table features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 70062 num_examples: 146 download_size: 10654 dataset_size: 70062 - config_name: reasoning_about_colored_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 89579 num_examples: 250 download_size: 20387 dataset_size: 89579 - config_name: ruin_names features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 46469 num_examples: 250 download_size: 15475 dataset_size: 46469 - config_name: salient_translation_error_detection features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 277110 num_examples: 250 download_size: 56862 dataset_size: 277110 - config_name: snarks features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38223 num_examples: 178 download_size: 16406 dataset_size: 38223 - config_name: sports_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 22723 num_examples: 250 download_size: 8163 dataset_size: 22723 - config_name: temporal_sequences features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 139546 num_examples: 250 download_size: 35571 dataset_size: 139546 - config_name: tracking_shuffled_objects_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 162590 num_examples: 250 download_size: 37111 dataset_size: 162590 - config_name: tracking_shuffled_objects_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 207274 num_examples: 250 download_size: 49062 dataset_size: 207274 - config_name: tracking_shuffled_objects_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 122104 num_examples: 250 download_size: 25142 dataset_size: 122104 - config_name: web_of_lies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 47582 num_examples: 250 download_size: 15615 dataset_size: 47582 - config_name: word_sorting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 60918 num_examples: 250 download_size: 44584 dataset_size: 60918 configs: - config_name: boolean_expressions data_files: - split: test path: boolean_expressions/test-* - config_name: causal_judgement data_files: - split: test path: causal_judgement/test-* - config_name: date_understanding data_files: - split: test path: date_understanding/test-* - config_name: default data_files: - split: test path: data/test-* - config_name: disambiguation_qa data_files: - split: test path: disambiguation_qa/test-* - config_name: dyck_languages data_files: - split: test path: dyck_languages/test-* - config_name: formal_fallacies data_files: - split: test path: formal_fallacies/test-* - config_name: geometric_shapes data_files: - split: test path: geometric_shapes/test-* - config_name: hyperbaton data_files: - split: test path: hyperbaton/test-* - config_name: logical_deduction_five_objects data_files: - split: test path: logical_deduction_five_objects/test-* - config_name: logical_deduction_seven_objects data_files: - split: test path: logical_deduction_seven_objects/test-* - config_name: logical_deduction_three_objects data_files: - split: test path: logical_deduction_three_objects/test-* - config_name: movie_recommendation data_files: - split: test path: movie_recommendation/test-* - config_name: multistep_arithmetic_two data_files: - split: test path: multistep_arithmetic_two/test-* - config_name: navigate data_files: - split: test path: navigate/test-* - config_name: object_counting data_files: - split: test path: object_counting/test-* - config_name: penguins_in_a_table data_files: - split: test path: penguins_in_a_table/test-* - config_name: reasoning_about_colored_objects data_files: - split: test path: reasoning_about_colored_objects/test-* - config_name: ruin_names data_files: - split: test path: ruin_names/test-* - config_name: salient_translation_error_detection data_files: - split: test path: salient_translation_error_detection/test-* - config_name: snarks data_files: - split: test path: snarks/test-* - config_name: sports_understanding data_files: - split: test path: sports_understanding/test-* - config_name: temporal_sequences data_files: - split: test path: temporal_sequences/test-* - config_name: tracking_shuffled_objects_five_objects data_files: - split: test path: tracking_shuffled_objects_five_objects/test-* - config_name: tracking_shuffled_objects_seven_objects data_files: - split: test path: tracking_shuffled_objects_seven_objects/test-* - config_name: tracking_shuffled_objects_three_objects data_files: - split: test path: tracking_shuffled_objects_three_objects/test-* - config_name: web_of_lies data_files: - split: test path: web_of_lies/test-* - config_name: word_sorting data_files: - split: test path: word_sorting/test-* ---
argilla/databricks-dolly-15k-curated-en	argilla	"2023-10-02T12:32:53"	1,972,100	44	[ "language:en", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	null	"2023-05-30T09:54:44"	--- language: - en --- ## Guidelines In this dataset, you will find a collection of records that show a category, an instruction, a context and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible. To curate the dataset, you will need to provide an answer to the following text fields: 1 - Final instruction: The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record. 2 - Final context: The final version of the instruction field. You may copy it using the copy icon in the context field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an context to be completed, leave this question blank. 3 - Final response: The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above. You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard. ## Fields * `id` is of type <class 'str'> * `category` is of type <class 'str'> * `original-instruction` is of type <class 'str'> * `original-context` is of type <class 'str'> * `original-response` is of type <class 'str'> ## Questions * `new-instruction` : Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here. * `new-context` : Write the final version of the context, making sure that it makes sense with the task category. If the original context is ok, copy and paste it here. If an context is not needed, leave this empty. * `new-response` : Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and context) provided. If the original response is ok, copy and paste it here. ## Load with Argilla To load this dataset with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface('argilla/databricks-dolly-15k-curated-en') ``` ## Load with Datasets To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset('argilla/databricks-dolly-15k-curated-en') ```
lavita/medical-qa-shared-task-v1-toy	lavita	"2023-07-20T00:29:06"	1,523,272	17	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	null	"2023-07-20T00:28:51"	--- dataset_info: features: - name: id dtype: int64 - name: ending0 dtype: string - name: ending1 dtype: string - name: ending2 dtype: string - name: ending3 dtype: string - name: ending4 dtype: string - name: label dtype: int64 - name: sent1 dtype: string - name: sent2 dtype: string - name: startphrase dtype: string splits: - name: train num_bytes: 52480.01886421694 num_examples: 32 - name: dev num_bytes: 52490.64150943396 num_examples: 32 download_size: 89680 dataset_size: 104970.6603736509 --- # Dataset Card for "medical-qa-shared-task-v1-toy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mteb/banking77	mteb	"2022-09-27T19:15:02"	934,717	1	[ "language:en", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	null	"2022-05-17T12:14:06"	--- language: - en ---
ceval/ceval-exam	ceval	"2023-08-31T14:04:10"	826,634	240	[ "task_categories:text-classification", "task_categories:multiple-choice", "task_categories:question-answering", "language:zh", "license:cc-by-nc-sa-4.0", "size_categories:10K<n<100K", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2305.08322", "region:us" ]	[ "text-classification", "multiple-choice", "question-answering" ]	"2023-05-16T01:47:44"	--- license: cc-by-nc-sa-4.0 task_categories: - text-classification - multiple-choice - question-answering language: - zh pretty_name: C-Eval size_categories: - 10K<n<100K --- C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https://arxiv.org/abs/2305.08322) for more details. Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/SJTU-LIT/ceval/tree/main#how-to-submit) ### Load the data ```python from datasets import load_dataset dataset=load_dataset(r"ceval/ceval-exam",name="computer_network") print(dataset['val'][0]) # {'id': 0, 'question': '使用位填充方法，以01111110为位首flag，数据为011011111111111111110010，求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ''} ``` More details on loading and using the data are at our [github page](https://github.com/SJTU-LIT/ceval#data). Please cite our paper if you use our dataset. ``` @article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, journal={arXiv preprint arXiv:2305.08322}, year={2023} } ```
EleutherAI/hendrycks_math	EleutherAI	"2023-11-02T14:48:57"	737,801	8	[ "license:mit", "region:us" ]	null	"2023-09-14T20:28:56"	--- license: mit ---
HAERAE-HUB/KMMLU	HAERAE-HUB	"2024-03-05T14:13:32"	685,530	51	[ "task_categories:multiple-choice", "language:ko", "license:cc-by-nd-4.0", "size_categories:100K<n<1M", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2402.11548", "region:us", "mmlu", "haerae" ]	[ "multiple-choice" ]	"2023-11-27T09:06:18"	--- configs: - config_name: Accounting data_files: - split: train path: data/Accounting-train.csv - split: dev path: data/Accounting-dev.csv - split: test path: data/Accounting-test.csv - config_name: Agricultural-Sciences data_files: - split: train path: data/Agricultural-Sciences-train.csv - split: dev path: data/Agricultural-Sciences-dev.csv - split: test path: data/Agricultural-Sciences-test.csv - config_name: Aviation-Engineering-and-Maintenance data_files: - split: train path: data/Aviation-Engineering-and-Maintenance-train.csv - split: dev path: data/Aviation-Engineering-and-Maintenance-dev.csv - split: test path: data/Aviation-Engineering-and-Maintenance-test.csv - config_name: Biology data_files: - split: train path: data/Biology-train.csv - split: dev path: data/Biology-dev.csv - split: test path: data/Biology-test.csv - config_name: Chemical-Engineering data_files: - split: train path: data/Chemical-Engineering-train.csv - split: dev path: data/Chemical-Engineering-dev.csv - split: test path: data/Chemical-Engineering-test.csv - config_name: Chemistry data_files: - split: train path: data/Chemistry-train.csv - split: dev path: data/Chemistry-dev.csv - split: test path: data/Chemistry-test.csv - config_name: Civil-Engineering data_files: - split: train path: data/Civil-Engineering-train.csv - split: dev path: data/Civil-Engineering-dev.csv - split: test path: data/Civil-Engineering-test.csv - config_name: Computer-Science data_files: - split: train path: data/Computer-Science-train.csv - split: dev path: data/Computer-Science-dev.csv - split: test path: data/Computer-Science-test.csv - config_name: Construction data_files: - split: train path: data/Construction-train.csv - split: dev path: data/Construction-dev.csv - split: test path: data/Construction-test.csv - config_name: Criminal-Law data_files: - split: train path: data/Criminal-Law-train.csv - split: dev path: data/Criminal-Law-dev.csv - split: test path: data/Criminal-Law-test.csv - config_name: Ecology data_files: - split: train path: data/Ecology-train.csv - split: dev path: data/Ecology-dev.csv - split: test path: data/Ecology-test.csv - config_name: Economics data_files: - split: train path: data/Economics-train.csv - split: dev path: data/Economics-dev.csv - split: test path: data/Economics-test.csv - config_name: Education data_files: - split: train path: data/Education-train.csv - split: dev path: data/Education-dev.csv - split: test path: data/Education-test.csv - config_name: Electrical-Engineering data_files: - split: train path: data/Electrical-Engineering-train.csv - split: dev path: data/Electrical-Engineering-dev.csv - split: test path: data/Electrical-Engineering-test.csv - config_name: Electronics-Engineering data_files: - split: train path: data/Electronics-Engineering-train.csv - split: dev path: data/Electronics-Engineering-dev.csv - split: test path: data/Electronics-Engineering-test.csv - config_name: Energy-Management data_files: - split: train path: data/Energy-Management-train.csv - split: dev path: data/Energy-Management-dev.csv - split: test path: data/Energy-Management-test.csv - config_name: Environmental-Science data_files: - split: train path: data/Environmental-Science-train.csv - split: dev path: data/Environmental-Science-dev.csv - split: test path: data/Environmental-Science-test.csv - config_name: Fashion data_files: - split: train path: data/Fashion-train.csv - split: dev path: data/Fashion-dev.csv - split: test path: data/Fashion-test.csv - config_name: Food-Processing data_files: - split: train path: data/Food-Processing-train.csv - split: dev path: data/Food-Processing-dev.csv - split: test path: data/Food-Processing-test.csv - config_name: Gas-Technology-and-Engineering data_files: - split: train path: data/Gas-Technology-and-Engineering-train.csv - split: dev path: data/Gas-Technology-and-Engineering-dev.csv - split: test path: data/Gas-Technology-and-Engineering-test.csv - config_name: Geomatics data_files: - split: train path: data/Geomatics-train.csv - split: dev path: data/Geomatics-dev.csv - split: test path: data/Geomatics-test.csv - config_name: Health data_files: - split: train path: data/Health-train.csv - split: dev path: data/Health-dev.csv - split: test path: data/Health-test.csv - config_name: Industrial-Engineer data_files: - split: train path: data/Industrial-Engineer-train.csv - split: dev path: data/Industrial-Engineer-dev.csv - split: test path: data/Industrial-Engineer-test.csv - config_name: Information-Technology data_files: - split: train path: data/Information-Technology-train.csv - split: dev path: data/Information-Technology-dev.csv - split: test path: data/Information-Technology-test.csv - config_name: Interior-Architecture-and-Design data_files: - split: train path: data/Interior-Architecture-and-Design-train.csv - split: dev path: data/Interior-Architecture-and-Design-dev.csv - split: test path: data/Interior-Architecture-and-Design-test.csv - config_name: Law data_files: - split: train path: data/Law-train.csv - split: dev path: data/Law-dev.csv - split: test path: data/Law-test.csv - config_name: Machine-Design-and-Manufacturing data_files: - split: train path: data/Machine-Design-and-Manufacturing-train.csv - split: dev path: data/Machine-Design-and-Manufacturing-dev.csv - split: test path: data/Machine-Design-and-Manufacturing-test.csv - config_name: Management data_files: - split: train path: data/Management-train.csv - split: dev path: data/Management-dev.csv - split: test path: data/Management-test.csv - config_name: Maritime-Engineering data_files: - split: train path: data/Maritime-Engineering-train.csv - split: dev path: data/Maritime-Engineering-dev.csv - split: test path: data/Maritime-Engineering-test.csv - config_name: Marketing data_files: - split: train path: data/Marketing-train.csv - split: dev path: data/Marketing-dev.csv - split: test path: data/Marketing-test.csv - config_name: Materials-Engineering data_files: - split: train path: data/Materials-Engineering-train.csv - split: dev path: data/Materials-Engineering-dev.csv - split: test path: data/Materials-Engineering-test.csv - config_name: Mechanical-Engineering data_files: - split: train path: data/Mechanical-Engineering-train.csv - split: dev path: data/Mechanical-Engineering-dev.csv - split: test path: data/Mechanical-Engineering-test.csv - config_name: Nondestructive-Testing data_files: - split: train path: data/Nondestructive-Testing-train.csv - split: dev path: data/Nondestructive-Testing-dev.csv - split: test path: data/Nondestructive-Testing-test.csv - config_name: Patent data_files: - split: train path: data/Patent-train.csv - split: dev path: data/Patent-dev.csv - split: test path: data/Patent-test.csv - config_name: Political-Science-and-Sociology data_files: - split: train path: data/Political-Science-and-Sociology-train.csv - split: dev path: data/Political-Science-and-Sociology-dev.csv - split: test path: data/Political-Science-and-Sociology-test.csv - config_name: Psychology data_files: - split: train path: data/Psychology-train.csv - split: dev path: data/Psychology-dev.csv - split: test path: data/Psychology-test.csv - config_name: Public-Safety data_files: - split: train path: data/Public-Safety-train.csv - split: dev path: data/Public-Safety-dev.csv - split: test path: data/Public-Safety-test.csv - config_name: Railway-and-Automotive-Engineering data_files: - split: train path: data/Railway-and-Automotive-Engineering-train.csv - split: dev path: data/Railway-and-Automotive-Engineering-dev.csv - split: test path: data/Railway-and-Automotive-Engineering-test.csv - config_name: Real-Estate data_files: - split: train path: data/Real-Estate-train.csv - split: dev path: data/Real-Estate-dev.csv - split: test path: data/Real-Estate-test.csv - config_name: Refrigerating-Machinery data_files: - split: train path: data/Refrigerating-Machinery-train.csv - split: dev path: data/Refrigerating-Machinery-dev.csv - split: test path: data/Refrigerating-Machinery-test.csv - config_name: Social-Welfare data_files: - split: train path: data/Social-Welfare-train.csv - split: dev path: data/Social-Welfare-dev.csv - split: test path: data/Social-Welfare-test.csv - config_name: Taxation data_files: - split: train path: data/Taxation-train.csv - split: dev path: data/Taxation-dev.csv - split: test path: data/Taxation-test.csv - config_name: Telecommunications-and-Wireless-Technology data_files: - split: train path: data/Telecommunications-and-Wireless-Technology-train.csv - split: dev path: data/Telecommunications-and-Wireless-Technology-dev.csv - split: test path: data/Telecommunications-and-Wireless-Technology-test.csv - config_name: Korean-History data_files: - split: train path: data/korean-history-train.csv - split: dev path: data/korean-history-dev.csv - split: test path: data/korean-history-test.csv - config_name: Math data_files: - split: train path: data/math-train.csv - split: dev path: data/math-dev.csv - split: test path: data/math-test.csv task_categories: - multiple-choice language: - ko tags: - mmlu - haerae size_categories: - 10K<n<100K license: cc-by-nd-4.0 --- # KMMLU (Korean-MMLU) We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks that are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 26 publically available and proprietary LLMs, identifying significant room for improvement. The best publicly available model achieves 50.54% on KMMLU, far below the average human performance of 62.6%. This model was primarily trained for English and Chinese, not Korean. Current LLMs tailored to Korean, such as Polyglot-Ko, perform far worse. Surprisingly, even the most capable proprietary LLMs, e.g., GPT-4 and HyperCLOVA X, achieve 59.95% and 53.40%, respectively. This suggests that further work is needed to improve Korean LLMs, and KMMLU offers the right tool to track this progress. We make our dataset publicly available on the Hugging Face Hub and integrate the benchmark into EleutherAI's Language Model Evaluation Harness. Link to Paper: [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://arxiv.org/abs/2402.11548) ### KMMLU Statistics \| Category \| # Questions \| \|------------------------------\|-------------\| \| Prerequisites \| \| \| None \| 59,909 \| \| 1 Prerequisite Test \| 12,316 \| \| 2 Prerequisite Tests \| 776 \| \| 2+ Years of Experience \| 65,135 \| \| 4+ Years of Experience \| 98,678 \| \| 9+ Years of Experience \| 6,963 \| \| Question Type \| \| \| Positive \| 207,030 \| \| Negation \| 36,777 \| \| Split \| \| \| Train \| 208,522 \| \| Validation \| 225 \| \| Test \| 35,030 \| \| Total \| 243,777 \| ### Categories To reimplement the categories in the paper, refer to the following: ``` supercategories = { "accounting": "HUMSS", "agricultural_sciences": "Other", "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_engineering": "STEM", "electronics_engineering": "Applied Science", "energy_management": "Applied Science", "environmental_science": "Applied Science", "fashion": "Other", "food_processing": "Other", "gas_technology_and_engineering": "Applied Science", "geomatics": "Applied Science", "health": "Other", "industrial_engineer": "Applied Science", "information_technology": "STEM", "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "patent": "Other", "political_science_and_sociology": "HUMSS", "psychology": "HUMSS", "public_safety": "Other", "railway_and_automotive_engineering": "Applied Science", "real_estate": "Other", "refrigerating_machinery": "Other", "social_welfare": "HUMSS", "taxation": "HUMSS", "telecommunications_and_wireless_technology": "Applied Science", "korean_history": "HUMSS", "math": "STEM" } ``` ### Point of Contact For any questions contact us via the following email:) ``` spthsrbwls123@yonsei.ac.kr ```
lighteval/MATH-Hard	lighteval	"2024-06-12T13:00:08"	672,558	13	[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2103.03874", "region:us", "explanation-generation" ]	[ "text2text-generation" ]	"2024-06-12T09:59:43"	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual source_datasets: - original task_categories: - text2text-generation task_ids: [] pretty_name: Mathematics Aptitude Test of Heuristics (MATH) tags: - explanation-generation dataset_info: features: - name: problem dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string configs: - config_name: default data_files: - split: train path: train/* - split: test path: test/* - config_name: algebra data_files: - split: train path: train/algebra.jsonl - split: test path: test/algebra.jsonl - config_name: counting_and_probability data_files: - split: train path: train/counting_and_probability.jsonl - split: test path: test/counting_and_probability.jsonl - config_name: geometry data_files: - split: train path: train/geometry.jsonl - split: test path: test/geometry.jsonl - config_name: intermediate_algebra data_files: - split: train path: train/intermediate_algebra.jsonl - split: test path: test/intermediate_algebra.jsonl - config_name: number_theory data_files: - split: train path: train/number_theory.jsonl - split: test path: test/number_theory.jsonl - config_name: prealgebra data_files: - split: train path: train/prealgebra.jsonl - split: test path: test/prealgebra.jsonl - config_name: precalculus data_files: - split: train path: train/precalculus.jsonl - split: test path: test/precalculus.jsonl --- # Dataset Card for Mathematics Aptitude Test of Heuristics, hard subset (MATH-Hard) dataset ## Dataset Description - Homepage: https://github.com/hendrycks/math - Repository: https://github.com/hendrycks/math - Paper: https://arxiv.org/pdf/2103.03874.pdf - Leaderboard: N/A - Point of Contact: Dan Hendrycks ### Dataset Summary The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations. For MATH-Hard, only the hardest questions were kept (Level 5). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's `\boxed` tag. An example from the dataset is: ``` {'problem': 'A board game spinner is divided into three parts labeled $A$, $B$ and $C$. The probability of the spinner landing on $A$ is $\\frac{1}{3}$ and the probability of the spinner landing on $B$ is $\\frac{5}{12}$. What is the probability of the spinner landing on $C$? Express your answer as a common fraction.', 'level': 'Level 1', 'type': 'Counting & Probability', 'solution': 'The spinner is guaranteed to land on exactly one of the three regions, so we know that the sum of the probabilities of it landing in each region will be 1. If we let the probability of it landing in region $C$ be $x$, we then have the equation $1 = \\frac{5}{12}+\\frac{1}{3}+x$, from which we have $x=\\boxed{\\frac{1}{4}}$.'} ``` ### Data Fields * `problem`: The competition math problem. * `solution`: The step-by-step solution. * `level`: We only kept tasks tagged as 'Level 5', the hardest level for the dataset. * `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus. ### Licensing Information https://github.com/hendrycks/math/blob/main/LICENSE ### Citation Information ```bibtex @article{hendrycksmath2021, title={Measuring Mathematical Problem Solving With the MATH Dataset}, author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, journal={arXiv preprint arXiv:2103.03874}, year={2021} } ```
cais/mmlu	cais	"2024-03-08T20:36:26"	566,256	311	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2009.03300", "arxiv:2005.00700", "arxiv:2005.14165", "arxiv:2008.02275", "region:us" ]	[ "question-answering" ]	"2022-03-02T23:29:22"	--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: mmlu pretty_name: Measuring Massive Multitask Language Understanding language_bcp47: - en-US dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 17143 dataset_size: 57303.3562203159 - config_name: all features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 6967453 num_examples: 14042 - name: validation num_bytes: 763484 num_examples: 1531 - name: dev num_bytes: 125353 num_examples: 285 - name: auxiliary_train num_bytes: 161000625 num_examples: 99842 download_size: 51503402 dataset_size: 168856915 - config_name: anatomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 66985.19833357072 num_examples: 135 - name: validation num_bytes: 6981.5649902024825 num_examples: 14 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 28864 dataset_size: 76165.9387623697 - config_name: astronomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 75420.3714570574 num_examples: 152 - name: validation num_bytes: 7978.931417374265 num_examples: 16 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 39316 dataset_size: 85598.47831302814 - config_name: auxiliary_train features: - name: train struct: - name: answer dtype: int64 - name: choices sequence: string - name: question dtype: string - name: subject dtype: string splits: - name: train num_bytes: 161000625 num_examples: 99842 download_size: 47518592 dataset_size: 161000625 - config_name: business_ethics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 31619 dataset_size: 57303.3562203159 - config_name: clinical_knowledge features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 131489.4633955277 num_examples: 265 - name: validation num_bytes: 14461.813193990856 num_examples: 29 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 51655 dataset_size: 148150.45202811505 - config_name: college_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 71450.87822247542 num_examples: 144 - name: validation num_bytes: 7978.931417374265 num_examples: 16 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 43017 dataset_size: 81628.98507844617 - config_name: college_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 3989.4657086871325 num_examples: 8 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 26781 dataset_size: 55807.30657955822 - config_name: college_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 41132 dataset_size: 57303.3562203159 - config_name: college_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 26779 dataset_size: 57303.3562203159 - config_name: college_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 85840.29119783506 num_examples: 173 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 56303 dataset_size: 99010.49733532117 - config_name: college_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 50611.0387409201 num_examples: 102 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 29539 dataset_size: 58295.7295289614 - config_name: computer_security features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 30150 dataset_size: 57303.3562203159 - config_name: conceptual_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 116603.86376584532 num_examples: 235 - name: validation num_bytes: 12965.76355323318 num_examples: 26 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 34968 dataset_size: 131768.802757675 - config_name: econometrics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 56565.27859279305 num_examples: 114 - name: validation num_bytes: 5984.198563030699 num_examples: 12 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 36040 dataset_size: 64748.652594420244 - config_name: electrical_engineering features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 71947.06487679818 num_examples: 145 - name: validation num_bytes: 7978.931417374265 num_examples: 16 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 26746 dataset_size: 82125.17173276893 - config_name: elementary_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 187558.555333998 num_examples: 378 - name: validation num_bytes: 20446.011757021555 num_examples: 41 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 54987 dataset_size: 210203.74252961605 - config_name: formal_logic features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 62519.518444666 num_examples: 126 - name: validation num_bytes: 6981.5649902024825 num_examples: 14 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 32884 dataset_size: 71700.25887346498 - config_name: global_facts features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 4986.8321358589155 num_examples: 10 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 19258 dataset_size: 56804.67300673001 - config_name: high_school_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 153817.86284005127 num_examples: 310 - name: validation num_bytes: 15957.86283474853 num_examples: 32 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 78216 dataset_size: 171974.90111339628 - config_name: high_school_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 100725.89082751745 num_examples: 203 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 45799 dataset_size: 113896.09696500355 - config_name: high_school_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 4488.148922273024 num_examples: 9 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 39072 dataset_size: 56305.989793144116 - config_name: high_school_european_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 81870.79796325309 num_examples: 165 - name: validation num_bytes: 8976.297844546049 num_examples: 18 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 196270 dataset_size: 93046.27124639563 - config_name: high_school_geography features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 98244.95755590372 num_examples: 198 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 38255 dataset_size: 111415.16369338983 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 95764.02428428999 num_examples: 193 - name: validation num_bytes: 10472.347485303722 num_examples: 21 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 52963 dataset_size: 108435.5472081902 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 193512.79518587096 num_examples: 390 - name: validation num_bytes: 21443.378184193338 num_examples: 43 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 68758 dataset_size: 217155.34880866078 - config_name: high_school_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 133970.39666714144 num_examples: 270 - name: validation num_bytes: 14461.813193990856 num_examples: 29 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 45210 dataset_size: 150631.38529972878 - config_name: high_school_microeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 118092.42372881356 num_examples: 238 - name: validation num_bytes: 12965.76355323318 num_examples: 26 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 49885 dataset_size: 133257.36272064323 - config_name: high_school_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 74924.18480273466 num_examples: 151 - name: validation num_bytes: 8477.614630960157 num_examples: 17 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 45483 dataset_size: 85600.9748722913 - config_name: high_school_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 270421.7266058966 num_examples: 545 - name: validation num_bytes: 29920.992815153495 num_examples: 60 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 113158 dataset_size: 302541.8948596466 - config_name: high_school_statistics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 107176.31733371314 num_examples: 216 - name: validation num_bytes: 11469.713912475507 num_examples: 23 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 74924 dataset_size: 120845.20668478514 - config_name: high_school_us_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 101222.0774818402 num_examples: 204 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 200043 dataset_size: 114392.2836193263 - config_name: high_school_world_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 117596.23707449081 num_examples: 237 - name: validation num_bytes: 12965.76355323318 num_examples: 26 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 250302 dataset_size: 132761.17606632048 - config_name: human_aging features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 110649.62391397236 num_examples: 223 - name: validation num_bytes: 11469.713912475507 num_examples: 23 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 41196 dataset_size: 124318.51326504436 - config_name: human_sexuality features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 65000.451716279735 num_examples: 131 - name: validation num_bytes: 5984.198563030699 num_examples: 12 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 32533 dataset_size: 73183.82571790692 - config_name: international_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 60038.58517305227 num_examples: 121 - name: validation num_bytes: 6482.88177661659 num_examples: 13 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 41592 dataset_size: 68720.64238826535 - config_name: jurisprudence features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 53588.15866685657 num_examples: 108 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 33578 dataset_size: 61272.84945489787 - config_name: logical_fallacies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 80878.4246546076 num_examples: 163 - name: validation num_bytes: 8976.297844546049 num_examples: 18 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 33669 dataset_size: 92053.89793775014 - config_name: machine_learning features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 55572.90528414756 num_examples: 112 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 31121 dataset_size: 63257.596072188855 - config_name: management features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 51107.225395242844 num_examples: 103 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 22828 dataset_size: 58791.91618328414 - config_name: marketing features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 116107.67711152257 num_examples: 234 - name: validation num_bytes: 12467.08033964729 num_examples: 25 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 49747 dataset_size: 130773.93288976635 - config_name: medical_genetics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 25775 dataset_size: 57303.3562203159 - config_name: miscellaneous features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 388514.15033471014 num_examples: 783 - name: validation num_bytes: 42886.756368386676 num_examples: 86 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 115097 dataset_size: 433600.08214169333 - config_name: moral_disputes features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 171680.58239567012 num_examples: 346 - name: validation num_bytes: 18949.96211626388 num_examples: 38 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 76043 dataset_size: 192829.71995053047 - config_name: moral_scenarios features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 444087.05561885773 num_examples: 895 - name: validation num_bytes: 49868.32135858916 num_examples: 100 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 109869 dataset_size: 496154.5524160434 - config_name: nutrition features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 151833.1162227603 num_examples: 306 - name: validation num_bytes: 16456.54604833442 num_examples: 33 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 69050 dataset_size: 170488.8377096912 - config_name: philosophy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 154314.04949437402 num_examples: 311 - name: validation num_bytes: 16955.229261920314 num_examples: 34 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 61912 dataset_size: 173468.45419489083 - config_name: prehistory features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 160764.47600056973 num_examples: 324 - name: validation num_bytes: 17453.912475506204 num_examples: 35 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 68826 dataset_size: 180417.5639146724 - config_name: professional_accounting features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 139924.6365190144 num_examples: 282 - name: validation num_bytes: 15459.179621162639 num_examples: 31 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 87297 dataset_size: 157582.99157877354 - config_name: professional_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 761150.3277310925 num_examples: 1534 - name: validation num_bytes: 84776.14630960157 num_examples: 170 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 1167828 dataset_size: 848125.6494792906 - config_name: professional_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 134962.7699757869 num_examples: 272 - name: validation num_bytes: 15459.179621162639 num_examples: 31 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 153242 dataset_size: 152621.12503554605 - config_name: professional_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 303666.2324455206 num_examples: 612 - name: validation num_bytes: 34409.14173742652 num_examples: 69 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 159357 dataset_size: 340274.5496215436 - config_name: public_relations features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 54580.53197550207 num_examples: 110 - name: validation num_bytes: 5984.198563030699 num_examples: 12 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 31500 dataset_size: 62763.90597712925 - config_name: security_studies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 121565.73030907278 num_examples: 245 - name: validation num_bytes: 13464.446766819072 num_examples: 27 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 140258 dataset_size: 137229.35251448833 - config_name: sociology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 99733.51751887196 num_examples: 201 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 56480 dataset_size: 112903.72365635807 - config_name: us_foreign_policy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 29027 dataset_size: 57303.3562203159 - config_name: virology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 82366.98461757584 num_examples: 166 - name: validation num_bytes: 8976.297844546049 num_examples: 18 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 38229 dataset_size: 93542.45790071838 - config_name: world_religions features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 84847.91788918957 num_examples: 171 - name: validation num_bytes: 9474.98105813194 num_examples: 19 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 27165 dataset_size: 96522.07438591801 configs: - config_name: abstract_algebra data_files: - split: test path: abstract_algebra/test-* - split: validation path: abstract_algebra/validation-* - split: dev path: abstract_algebra/dev-* - config_name: all data_files: - split: test path: all/test-* - split: validation path: all/validation-* - split: dev path: all/dev-* - split: auxiliary_train path: all/auxiliary_train-* - config_name: anatomy data_files: - split: test path: anatomy/test-* - split: validation path: anatomy/validation-* - split: dev path: anatomy/dev-* - config_name: astronomy data_files: - split: test path: astronomy/test-* - split: validation path: astronomy/validation-* - split: dev path: astronomy/dev-* - config_name: auxiliary_train data_files: - split: train path: auxiliary_train/train-* - config_name: business_ethics data_files: - split: test path: business_ethics/test-* - split: validation path: business_ethics/validation-* - split: dev path: business_ethics/dev-* - config_name: clinical_knowledge data_files: - split: test path: clinical_knowledge/test-* - split: validation path: clinical_knowledge/validation-* - split: dev path: clinical_knowledge/dev-* - config_name: college_biology data_files: - split: test path: college_biology/test-* - split: validation path: college_biology/validation-* - split: dev path: college_biology/dev-* - config_name: college_chemistry data_files: - split: test path: college_chemistry/test-* - split: validation path: college_chemistry/validation-* - split: dev path: college_chemistry/dev-* - config_name: college_computer_science data_files: - split: test path: college_computer_science/test-* - split: validation path: college_computer_science/validation-* - split: dev path: college_computer_science/dev-* - config_name: college_mathematics data_files: - split: test path: college_mathematics/test-* - split: validation path: college_mathematics/validation-* - split: dev path: college_mathematics/dev-* - config_name: college_medicine data_files: - split: test path: college_medicine/test-* - split: validation path: college_medicine/validation-* - split: dev path: college_medicine/dev-* - config_name: college_physics data_files: - split: test path: college_physics/test-* - split: validation path: college_physics/validation-* - split: dev path: college_physics/dev-* - config_name: computer_security data_files: - split: test path: computer_security/test-* - split: validation path: computer_security/validation-* - split: dev path: computer_security/dev-* - config_name: conceptual_physics data_files: - split: test path: conceptual_physics/test-* - split: validation path: conceptual_physics/validation-* - split: dev path: conceptual_physics/dev-* - config_name: econometrics data_files: - split: test path: econometrics/test-* - split: validation path: econometrics/validation-* - split: dev path: econometrics/dev-* - config_name: electrical_engineering data_files: - split: test path: electrical_engineering/test-* - split: validation path: electrical_engineering/validation-* - split: dev path: electrical_engineering/dev-* - config_name: elementary_mathematics data_files: - split: test path: elementary_mathematics/test-* - split: validation path: elementary_mathematics/validation-* - split: dev path: elementary_mathematics/dev-* - config_name: formal_logic data_files: - split: test path: formal_logic/test-* - split: validation path: formal_logic/validation-* - split: dev path: formal_logic/dev-* - config_name: global_facts data_files: - split: test path: global_facts/test-* - split: validation path: global_facts/validation-* - split: dev path: global_facts/dev-* - config_name: high_school_biology data_files: - split: test path: high_school_biology/test-* - split: validation path: high_school_biology/validation-* - split: dev path: high_school_biology/dev-* - config_name: high_school_chemistry data_files: - split: test path: high_school_chemistry/test-* - split: validation path: high_school_chemistry/validation-* - split: dev path: high_school_chemistry/dev-* - config_name: high_school_computer_science data_files: - split: test path: high_school_computer_science/test-* - split: validation path: high_school_computer_science/validation-* - split: dev path: high_school_computer_science/dev-* - config_name: high_school_european_history data_files: - split: test path: high_school_european_history/test-* - split: validation path: high_school_european_history/validation-* - split: dev path: high_school_european_history/dev-* - config_name: high_school_geography data_files: - split: test path: high_school_geography/test-* - split: validation path: high_school_geography/validation-* - split: dev path: high_school_geography/dev-* - config_name: high_school_government_and_politics data_files: - split: test path: high_school_government_and_politics/test-* - split: validation path: high_school_government_and_politics/validation-* - split: dev path: high_school_government_and_politics/dev-* - config_name: high_school_macroeconomics data_files: - split: test path: high_school_macroeconomics/test-* - split: validation path: high_school_macroeconomics/validation-* - split: dev path: high_school_macroeconomics/dev-* - config_name: high_school_mathematics data_files: - split: test path: high_school_mathematics/test-* - split: validation path: high_school_mathematics/validation-* - split: dev path: high_school_mathematics/dev-* - config_name: high_school_microeconomics data_files: - split: test path: high_school_microeconomics/test-* - split: validation path: high_school_microeconomics/validation-* - split: dev path: high_school_microeconomics/dev-* - config_name: high_school_physics data_files: - split: test path: high_school_physics/test-* - split: validation path: high_school_physics/validation-* - split: dev path: high_school_physics/dev-* - config_name: high_school_psychology data_files: - split: test path: high_school_psychology/test-* - split: validation path: high_school_psychology/validation-* - split: dev path: high_school_psychology/dev-* - config_name: high_school_statistics data_files: - split: test path: high_school_statistics/test-* - split: validation path: high_school_statistics/validation-* - split: dev path: high_school_statistics/dev-* - config_name: high_school_us_history data_files: - split: test path: high_school_us_history/test-* - split: validation path: high_school_us_history/validation-* - split: dev path: high_school_us_history/dev-* - config_name: high_school_world_history data_files: - split: test path: high_school_world_history/test-* - split: validation path: high_school_world_history/validation-* - split: dev path: high_school_world_history/dev-* - config_name: human_aging data_files: - split: test path: human_aging/test-* - split: validation path: human_aging/validation-* - split: dev path: human_aging/dev-* - config_name: human_sexuality data_files: - split: test path: human_sexuality/test-* - split: validation path: human_sexuality/validation-* - split: dev path: human_sexuality/dev-* - config_name: international_law data_files: - split: test path: international_law/test-* - split: validation path: international_law/validation-* - split: dev path: international_law/dev-* - config_name: jurisprudence data_files: - split: test path: jurisprudence/test-* - split: validation path: jurisprudence/validation-* - split: dev path: jurisprudence/dev-* - config_name: logical_fallacies data_files: - split: test path: logical_fallacies/test-* - split: validation path: logical_fallacies/validation-* - split: dev path: logical_fallacies/dev-* - config_name: machine_learning data_files: - split: test path: machine_learning/test-* - split: validation path: machine_learning/validation-* - split: dev path: machine_learning/dev-* - config_name: management data_files: - split: test path: management/test-* - split: validation path: management/validation-* - split: dev path: management/dev-* - config_name: marketing data_files: - split: test path: marketing/test-* - split: validation path: marketing/validation-* - split: dev path: marketing/dev-* - config_name: medical_genetics data_files: - split: test path: medical_genetics/test-* - split: validation path: medical_genetics/validation-* - split: dev path: medical_genetics/dev-* - config_name: miscellaneous data_files: - split: test path: miscellaneous/test-* - split: validation path: miscellaneous/validation-* - split: dev path: miscellaneous/dev-* - config_name: moral_disputes data_files: - split: test path: moral_disputes/test-* - split: validation path: moral_disputes/validation-* - split: dev path: moral_disputes/dev-* - config_name: moral_scenarios data_files: - split: test path: moral_scenarios/test-* - split: validation path: moral_scenarios/validation-* - split: dev path: moral_scenarios/dev-* - config_name: nutrition data_files: - split: test path: nutrition/test-* - split: validation path: nutrition/validation-* - split: dev path: nutrition/dev-* - config_name: philosophy data_files: - split: test path: philosophy/test-* - split: validation path: philosophy/validation-* - split: dev path: philosophy/dev-* - config_name: prehistory data_files: - split: test path: prehistory/test-* - split: validation path: prehistory/validation-* - split: dev path: prehistory/dev-* - config_name: professional_accounting data_files: - split: test path: professional_accounting/test-* - split: validation path: professional_accounting/validation-* - split: dev path: professional_accounting/dev-* - config_name: professional_law data_files: - split: test path: professional_law/test-* - split: validation path: professional_law/validation-* - split: dev path: professional_law/dev-* - config_name: professional_medicine data_files: - split: test path: professional_medicine/test-* - split: validation path: professional_medicine/validation-* - split: dev path: professional_medicine/dev-* - config_name: professional_psychology data_files: - split: test path: professional_psychology/test-* - split: validation path: professional_psychology/validation-* - split: dev path: professional_psychology/dev-* - config_name: public_relations data_files: - split: test path: public_relations/test-* - split: validation path: public_relations/validation-* - split: dev path: public_relations/dev-* - config_name: security_studies data_files: - split: test path: security_studies/test-* - split: validation path: security_studies/validation-* - split: dev path: security_studies/dev-* - config_name: sociology data_files: - split: test path: sociology/test-* - split: validation path: sociology/validation-* - split: dev path: sociology/dev-* - config_name: us_foreign_policy data_files: - split: test path: us_foreign_policy/test-* - split: validation path: us_foreign_policy/validation-* - split: dev path: us_foreign_policy/dev-* - config_name: virology data_files: - split: test path: virology/test-* - split: validation path: virology/validation-* - split: dev path: virology/dev-* - config_name: world_religions data_files: - split: test path: world_religions/test-* - split: validation path: world_religions/validation-* - split: dev path: world_religions/dev-* --- # Dataset Card for MMLU ## 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 - Repository: https://github.com/hendrycks/test - Paper: https://arxiv.org/abs/2009.03300 ### Dataset Summary [Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions'] ### Supported Tasks and Leaderboards \| Model \| Authors \| Humanities \| Social Science \| STEM \| Other \| Average \| \|------------------------------------\|----------\|:-------:\|:-------:\|:-------:\|:-------:\|:-------:\| \| [UnifiedQA](https://arxiv.org/abs/2005.00700) \| Khashabi et al., 2020 \| 45.6 \| 56.6 \| 40.2 \| 54.6 \| 48.9 \| [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) \| Brown et al., 2020 \| 40.8 \| 50.4 \| 36.7 \| 48.8 \| 43.9 \| [GPT-2](https://arxiv.org/abs/2005.14165) \| Radford et al., 2019 \| 32.8 \| 33.3 \| 30.2 \| 33.1 \| 32.4 \| Random Baseline \| N/A \| 25.0 \| 25.0 \| 25.0 \| 25.0 \| 25.0 \| 25.0 ### Languages English ## Dataset Structure ### Data Instances An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"], "answer": "D" } ``` ### Data Fields - `question`: a string feature - `choices`: a list of 4 string features - `answer`: a ClassLabel feature ### Data Splits - `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc. - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask \| \| auxiliary_train \| dev \| val \| test \| \| ----- \| :------: \| :-----: \| :-----: \| :-----: \| \| TOTAL \| 99842 \| 285 \| 1531 \| 14042 ## Dataset Creation ### Curation Rationale Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn. ### 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 [MIT License](https://github.com/hendrycks/test/blob/master/LICENSE) ### Citation Information If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from: ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ``` ### Contributions Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
lukaemon/bbh	lukaemon	"2023-02-02T01:14:46"	565,185	45	[ "size_categories:1K<n<10K", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]	null	"2023-02-01T07:46:51"	--- dataset_info: - config_name: boolean_expressions features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 11790 num_examples: 250 download_size: 17172 dataset_size: 11790 - config_name: causal_judgement features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 198021 num_examples: 187 download_size: 202943 dataset_size: 198021 - config_name: date_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 54666 num_examples: 250 download_size: 61760 dataset_size: 54666 - config_name: disambiguation_qa features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 78620 num_examples: 250 download_size: 85255 dataset_size: 78620 - config_name: dyck_languages features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38432 num_examples: 250 download_size: 43814 dataset_size: 38432 - config_name: formal_fallacies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 138224 num_examples: 250 download_size: 145562 dataset_size: 138224 - config_name: geometric_shapes features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 68560 num_examples: 250 download_size: 77242 dataset_size: 68560 - config_name: hyperbaton features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38574 num_examples: 250 download_size: 44706 dataset_size: 38574 - config_name: logical_deduction_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 148595 num_examples: 250 download_size: 155477 dataset_size: 148595 - config_name: logical_deduction_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 191022 num_examples: 250 download_size: 198404 dataset_size: 191022 - config_name: logical_deduction_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 105831 num_examples: 250 download_size: 112213 dataset_size: 105831 - config_name: movie_recommendation features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 50985 num_examples: 250 download_size: 57684 dataset_size: 50985 - config_name: multistep_arithmetic_two features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 12943 num_examples: 250 download_size: 18325 dataset_size: 12943 - config_name: navigate features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 49031 num_examples: 250 download_size: 55163 dataset_size: 49031 - config_name: object_counting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 30508 num_examples: 250 download_size: 35890 dataset_size: 30508 - config_name: penguins_in_a_table features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 70062 num_examples: 146 download_size: 74516 dataset_size: 70062 - config_name: reasoning_about_colored_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 89579 num_examples: 250 download_size: 98694 dataset_size: 89579 - config_name: ruin_names features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 46537 num_examples: 250 download_size: 53178 dataset_size: 46537 - config_name: salient_translation_error_detection features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 277110 num_examples: 250 download_size: 286443 dataset_size: 277110 - config_name: snarks features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38223 num_examples: 178 download_size: 42646 dataset_size: 38223 - config_name: sports_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 22723 num_examples: 250 download_size: 28617 dataset_size: 22723 - config_name: temporal_sequences features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 139546 num_examples: 250 download_size: 148176 dataset_size: 139546 - config_name: tracking_shuffled_objects_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 162590 num_examples: 250 download_size: 169722 dataset_size: 162590 - config_name: tracking_shuffled_objects_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 207274 num_examples: 250 download_size: 214906 dataset_size: 207274 - config_name: tracking_shuffled_objects_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 122104 num_examples: 250 download_size: 128736 dataset_size: 122104 - config_name: web_of_lies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 47582 num_examples: 250 download_size: 52964 dataset_size: 47582 - config_name: word_sorting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 60918 num_examples: 250 download_size: 66300 dataset_size: 60918 --- # BIG-bench Hard dataset homepage: https://github.com/suzgunmirac/BIG-Bench-Hard ``` @article{suzgun2022challenging, title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them}, author={Suzgun, Mirac and Scales, Nathan and Sch{\"a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V and Chi, Ed H and Zhou, Denny and and Wei, Jason}, journal={arXiv preprint arXiv:2210.09261}, year={2022} } ```
chansung/requested-arxiv-ids-3	chansung	"2024-05-15T21:10:31"	552,869	1	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	null	"2024-03-06T04:21:39"	--- dataset_info: features: - name: Requested arXiv IDs sequence: string splits: - name: train num_bytes: 7.5 num_examples: 1 download_size: 1042 dataset_size: 7.5 configs: - config_name: default data_files: - split: train path: data/train-* ---
lmms-lab/LMMs-Eval-Lite	lmms-lab	"2024-07-04T04:16:56"	552,775	2	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	null	"2024-06-27T03:29:05"	--- dataset_info: - config_name: ai2d features: - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image dtype: image splits: - name: lite num_bytes: 90543302.1658031 num_examples: 500 download_size: 81458737 dataset_size: 90543302.1658031 - config_name: chartqa features: - name: type dtype: string - name: question dtype: string - name: answer dtype: string - name: image dtype: image splits: - name: lite num_bytes: 23170424.2 num_examples: 500 download_size: 23219432 dataset_size: 23170424.2 - config_name: coco2017_cap_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answer sequence: string - name: id dtype: int64 - name: license dtype: int8 - name: file_name dtype: string - name: coco_url dtype: string - name: height dtype: int32 - name: width dtype: int32 - name: date_captured dtype: string splits: - name: lite num_bytes: 81724646.1 num_examples: 500 download_size: 81036195 dataset_size: 81724646.1 - config_name: docvqa_val features: - name: questionId dtype: string - name: question dtype: string - name: question_types sequence: string - name: image dtype: image - name: docId dtype: int64 - name: ucsf_document_id dtype: string - name: ucsf_document_page_no dtype: string - name: answers sequence: string - name: data_split dtype: string splits: - name: lite num_bytes: 334538449.19872874 num_examples: 500 download_size: 249349131 dataset_size: 334538449.19872874 - config_name: flickr30k_test features: - name: image dtype: image - name: caption sequence: string - name: sentids sequence: string - name: img_id dtype: string - name: filename dtype: string splits: - name: lite num_bytes: 69689341.17644653 num_examples: 500 download_size: 66621555 dataset_size: 69689341.17644653 - config_name: gqa features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: lite num_bytes: 243022.3008427413 num_examples: 500 download_size: 107530 dataset_size: 243022.3008427413 - config_name: infovqa_val features: - name: questionId dtype: string - name: question dtype: string - name: answers sequence: string - name: answer_type sequence: string - name: image dtype: image - name: image_url dtype: string - name: operation/reasoning sequence: string - name: ocr dtype: string - name: data_split dtype: string splits: - name: lite num_bytes: 304765105.6765441 num_examples: 500 download_size: 233689969 dataset_size: 304765105.6765441 - config_name: mmbench_cn_dev features: - name: index dtype: int64 - name: question dtype: string - name: hint dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string - name: L2-category dtype: string - name: comment dtype: string - name: split dtype: string splits: - name: lite num_bytes: 11861120.35112035 num_examples: 500 download_size: 12795903 dataset_size: 11861120.35112035 - config_name: mmbench_en_dev features: - name: index dtype: int64 - name: question dtype: string - name: hint dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string - name: L2-category dtype: string - name: comment dtype: string - name: split dtype: string splits: - name: lite num_bytes: 11871291.175791176 num_examples: 500 download_size: 12524588 dataset_size: 11871291.175791176 - config_name: nocaps_val features: - name: image dtype: image - name: image_coco_url dtype: string - name: image_date_captured dtype: string - name: image_file_name dtype: string - name: image_height dtype: int32 - name: image_width dtype: int32 - name: image_id dtype: int32 - name: image_license dtype: int8 - name: image_open_images_id dtype: string - name: annotations_ids sequence: int32 - name: annotations_captions sequence: string splits: - name: lite num_bytes: 157984760.66666666 num_examples: 500 download_size: 155545761 dataset_size: 157984760.66666666 - config_name: ok_vqa_val2014 features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answers sequence: string - name: question_type dtype: string - name: answer_type dtype: string splits: - name: lite num_bytes: 82607924.29647246 num_examples: 500 download_size: 80223931 dataset_size: 82607924.29647246 - config_name: refcoco_bbox_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answer sequence: string - name: segmentation sequence: float32 - name: bbox sequence: float32 - name: iscrowd dtype: int8 - name: file_name dtype: string splits: - name: lite num_bytes: 87885477.24435365 num_examples: 500 download_size: 88424601 dataset_size: 87885477.24435365 - config_name: seedbench features: - name: answer dtype: string - name: choice_a dtype: string - name: choice_b dtype: string - name: choice_c dtype: string - name: choice_d dtype: string - name: data_id dtype: string - name: data_type dtype: string - name: question dtype: string - name: question_id dtype: string - name: question_type_id dtype: int16 - name: image sequence: image - name: segment sequence: int64 splits: - name: lite num_bytes: 755921749.3379655 num_examples: 500 download_size: 181839440 dataset_size: 755921749.3379655 - config_name: textcaps_val features: - name: question_id dtype: string - name: question dtype: string - name: image dtype: image - name: image_id dtype: string - name: image_classes sequence: string - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: image_width dtype: int64 - name: image_height dtype: int64 - name: set_name dtype: string - name: image_name dtype: string - name: image_path dtype: string - name: caption_id sequence: int64 - name: caption_str sequence: string - name: reference_strs sequence: string splits: - name: lite num_bytes: 145274544.53569174 num_examples: 500 download_size: 135721574 dataset_size: 145274544.53569174 - config_name: textvqa_val features: - name: image_id dtype: string - name: question_id dtype: int32 - name: question dtype: string - name: question_tokens sequence: string - name: image dtype: image - name: image_width dtype: int32 - name: image_height dtype: int32 - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: answers sequence: string - name: image_classes sequence: string - name: set_name dtype: string - name: ocr_tokens sequence: string splits: - name: lite num_bytes: 143485382.6 num_examples: 500 download_size: 139843809 dataset_size: 143485382.6 - config_name: vizwiz_vqa_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answers sequence: string - name: category dtype: string splits: - name: lite num_bytes: 242880108.01111367 num_examples: 500 download_size: 232689462 dataset_size: 242880108.01111367 - config_name: vqav2_val features: - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: image_id dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: image dtype: image splits: - name: lite num_bytes: 79046522.98300941 num_examples: 500 download_size: 78981610 dataset_size: 79046522.98300941 configs: - config_name: ai2d data_files: - split: lite path: ai2d/lite-* - config_name: chartqa data_files: - split: lite path: chartqa/lite-* - config_name: coco2017_cap_val data_files: - split: lite path: coco2017_cap_val/lite-* - config_name: docvqa_val data_files: - split: lite path: docvqa_val/lite-* - config_name: flickr30k_test data_files: - split: lite path: flickr30k_test/lite-* - config_name: gqa data_files: - split: lite path: gqa/lite-* - config_name: infovqa_val data_files: - split: lite path: infovqa_val/lite-* - config_name: mmbench_cn_dev data_files: - split: lite path: mmbench_cn_dev/lite-* - config_name: mmbench_en_dev data_files: - split: lite path: mmbench_en_dev/lite-* - config_name: nocaps_val data_files: - split: lite path: nocaps_val/lite-* - config_name: ok_vqa_val2014 data_files: - split: lite path: ok_vqa_val2014/lite-* - config_name: refcoco_bbox_val data_files: - split: lite path: refcoco_bbox_val/lite-* - config_name: seedbench data_files: - split: lite path: seedbench/lite-* - config_name: textcaps_val data_files: - split: lite path: textcaps_val/lite-* - config_name: textvqa_val data_files: - split: lite path: textvqa_val/lite-* - config_name: vizwiz_vqa_val data_files: - split: lite path: vizwiz_vqa_val/lite-* - config_name: vqav2_val data_files: - split: lite path: vqav2_val/lite-* ---
trl-internal-testing/zen	trl-internal-testing	"2024-09-13T21:03:59"	544,189	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	null	"2024-09-13T21:03:47"	--- dataset_info: - config_name: conversational_implicit_prompt_preference features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2810.3684210526317 num_examples: 17 - name: test num_bytes: 253 num_examples: 2 download_size: 7230 dataset_size: 3063.3684210526317 - config_name: conversational_language_modeling features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1439.6315789473683 num_examples: 17 - name: test num_bytes: 169.3684210526316 num_examples: 2 download_size: 3950 dataset_size: 1609.0 - config_name: conversational_preference features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2116.0526315789475 num_examples: 17 - name: test num_bytes: 248.94736842105263 num_examples: 2 download_size: 9108 dataset_size: 2365.0 - config_name: conversational_prompt_completion features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1507.6315789473683 num_examples: 17 - name: test num_bytes: 177.3684210526316 num_examples: 2 download_size: 6364 dataset_size: 1685.0 - config_name: conversational_prompt_only features: - name: prompt list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 830.3157894736842 num_examples: 17 - name: test num_bytes: 97.6842105263158 num_examples: 2 download_size: 3676 dataset_size: 928.0 - config_name: conversational_unpaired_preference features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string - name: label dtype: bool splits: - name: train num_bytes: 1484.3684210526317 num_examples: 17 - name: test num_bytes: 174.6315789473684 num_examples: 2 download_size: 7196 dataset_size: 1659.0 - config_name: standard_implicit_prompt_preference features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 1606.0526315789473 num_examples: 17 - name: test num_bytes: 188.94736842105263 num_examples: 2 download_size: 4688 dataset_size: 1795.0 - config_name: standard_language_modeling features: - name: text dtype: string splits: - name: train num_bytes: 787.3684210526316 num_examples: 17 - name: test num_bytes: 92.63157894736842 num_examples: 2 download_size: 2626 dataset_size: 880.0 - config_name: standard_preference features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 1268.7368421052631 num_examples: 17 - name: test num_bytes: 149.26315789473685 num_examples: 2 download_size: 4953 dataset_size: 1418.0 - config_name: standard_prompt_completion features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 855.3684210526316 num_examples: 17 - name: test num_bytes: 100.63157894736842 num_examples: 2 download_size: 3473 dataset_size: 956.0 - config_name: standard_prompt_only features: - name: prompt dtype: string splits: - name: train num_bytes: 473.3157894736842 num_examples: 17 - name: test num_bytes: 55.68421052631579 num_examples: 2 download_size: 2160 dataset_size: 529.0 - config_name: standard_unpaired_preference features: - name: prompt dtype: string - name: completion dtype: string - name: label dtype: bool splits: - name: train num_bytes: 867.8947368421053 num_examples: 17 - name: test num_bytes: 102.10526315789474 num_examples: 2 download_size: 4364 dataset_size: 970.0 configs: - config_name: conversational_implicit_prompt_preference data_files: - split: train path: conversational_implicit_prompt_preference/train-* - split: test path: conversational_implicit_prompt_preference/test-* - config_name: conversational_language_modeling data_files: - split: train path: conversational_language_modeling/train-* - split: test path: conversational_language_modeling/test-* - config_name: conversational_preference data_files: - split: train path: conversational_preference/train-* - split: test path: conversational_preference/test-* - config_name: conversational_prompt_completion data_files: - split: train path: conversational_prompt_completion/train-* - split: test path: conversational_prompt_completion/test-* - config_name: conversational_prompt_only data_files: - split: train path: conversational_prompt_only/train-* - split: test path: conversational_prompt_only/test-* - config_name: conversational_unpaired_preference data_files: - split: train path: conversational_unpaired_preference/train-* - split: test path: conversational_unpaired_preference/test-* - config_name: standard_implicit_prompt_preference data_files: - split: train path: standard_implicit_prompt_preference/train-* - split: test path: standard_implicit_prompt_preference/test-* - config_name: standard_language_modeling data_files: - split: train path: standard_language_modeling/train-* - split: test path: standard_language_modeling/test-* - config_name: standard_preference data_files: - split: train path: standard_preference/train-* - split: test path: standard_preference/test-* - config_name: standard_prompt_completion data_files: - split: train path: standard_prompt_completion/train-* - split: test path: standard_prompt_completion/test-* - config_name: standard_prompt_only data_files: - split: train path: standard_prompt_only/train-* - split: test path: standard_prompt_only/test-* - config_name: standard_unpaired_preference data_files: - split: train path: standard_unpaired_preference/train-* - split: test path: standard_unpaired_preference/test-* ---
Hennara/ammlu	Hennara	"2024-03-02T17:20:25"	543,776	0	[ "task_categories:question-answering", "language:ar", "size_categories:10K<n<100K", "arxiv:2009.03300", "arxiv:2309.12053", "region:us" ]	[ "question-answering" ]	"2024-02-06T06:11:42"	--- task_categories: - question-answering language: - ar size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name Arabic MMLU: Measuring massive multitask language understanding in Arabic This dataset has been translated from the original MMLU with the help of GPT-4. The original data paper [MMLU](https://arxiv.org/pdf/2009.03300v3.pdf) The MMLU dataset on huggingface [MMLU](cais/mmlu) ### Dataset Sources [optional] The translation and re-generation has been done by AceGPT researchers [AceGPT](https://arxiv.org/abs/2309.12053) - [Repository:](https://github.com/FreedomIntelligence/AceGPT/tree/main/eval/benchmark_eval/benchmarks/MMLUArabic) - [Paper](https://arxiv.org/abs/2309.12053) ## Uses Arabic-MMLU is a comprehensive evaluation benchmark specifically designed to evaluate the knowledge and reasoning abilities of LLMs within the context of Arabic language and culture. Arabic-MMLU covers a wide range of subjects, comprising 57 topics that span from elementary to advanced professional levels. ### Direct Use This dataset is available to used directly using [datasets](https://github.com/huggingface/datasets) from huggingface, also is availabe to use with [lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) framework. ## Dataset Structure The dataset consist of 57 subject, divided into 4 category. \| Subject Area \| STEM \| Humanities \| Social Sciences \| Other \| \|---\|---\|---\|---\|---\| \| abstract_algebra \| ✓ \| \| \| \| \| anatomy \| ✓ \| \| \| \| \| astronomy \| ✓ \| \| \| \| \| business_ethics \| \| \| \| ✓ \| \| clinical_knowledge \| \| \| \| ✓ \| \| college_biology \| ✓ \| \| \| \| \| college_chemistry \| ✓ \| \| \| \| \| college_computer_science \| ✓ \| \| \| \| \| college_mathematics \| ✓ \| \| \| \| \| college_medicine \| \| \| \| ✓ \| \| college_physics \| ✓ \| \| \| \| \| computer_security \| ✓ \| \| \| \| \| conceptual_physics \| ✓ \| \| \| \| \| econometrics \| \| \| ✓ \| \| \| electrical_engineering \| ✓ \| \| \| \| \| elementary_mathematics \| ✓ \| \| \| \| \| formal_logic \| \| ✓ \| \| \| \| global_facts \| \| \| \| ✓ \| \| high_school_biology \| ✓ \| \| \| \| \| high_school_chemistry \| ✓ \| \| \| \| \| high_school_computer_science \| ✓ \| \| \| \| \| high_school_european_history \| \| ✓ \| \| \| \| high_school_geography \| \| \| ✓ \| \| \| high_school_government_and_politics \| \| \| ✓ \| \| \| high_school_macroeconomics \| \| \| ✓ \| \| \| high_school_mathematics \| ✓ \| \| \| \| \| high_school_microeconomics \| \| \| ✓ \| \| \| high_school_physics \| ✓ \| \| \| \| \| high_school_psychology \| \| \| ✓ \| \| \| high_school_statistics \| ✓ \| \| \| \| \| high_school_us_history \| \| ✓ \| \| \| \| high_school_world_history \| \| ✓ \| \| \| \| human_aging \| \| \| \| ✓ \| \| human_sexuality \| \| \| ✓ \| \| \| international_law \| \| ✓ \| \| \| \| jurisprudence \| \| ✓ \| \| \| \| logical_fallacies \| \| ✓ \| \| \| \| machine_learning \| ✓ \| \| \| \| \| management \| \| \| \| ✓ \| \| marketing \| \| \| \| ✓ \| \| medical_genetics \| \| \| \| ✓ \| \| miscellaneous \| \| \| \| ✓ \| \| moral_disputes \| \| ✓ \| \| \| \| moral_scenarios \| \| ✓ \| \| \| \| nutrition \| \| \| \| ✓ \| \| philosophy \| \| ✓ \| \| \| \| prehistory \| \| ✓ \| \| \| \| professional_accounting \| \| \| \| ✓ \| \| professional_law \| \| ✓ \| \| \| \| professional_medicine \| \| \| \| ✓ \| \| professional_psychology \| \| \| ✓ \| \| \| public_relations \| \| \| ✓ \| \| \| security_studies \| \| \| ✓ \| \| \| sociology \| \| \| ✓ \| \| \| us_foreign_policy \| \| \| ✓ \| \| \| virology \| \| \| \| ✓ \| \| world_religions \| \| ✓ \| \| \| \| - \| - \| - \| - \| - \| each item of the dataset is a dictionary with Question, A, B, C, D, Answer where A,B,C,D are options to the choose from. here is three example from the abstract algebra subject. \| Question \| A \| B \| C \| D \| Answer \| \|---\|---\|---\|---\|---\|---\| \| مجموعة فرعية H من مجموعة (G،) هي مجموعة إذا \| 'a، b في H => a b في H' \| 'a في H => a^-1 في H' \| 'a، b في H => a * b^-1 في H' \| 'H يحتوي على العنصر المحدد' \| C \| \| 'ما هو ترتيب العنصر (4، 2) من Z_12 x Z_8' \| 2 \| 4 \| 8 \| 12 \| C \| \|ما هو الدرجة لتمديد الحقل المعطى Q(sqrt(2) + sqrt(3)) على Q\| 0 \| 4 \| 2 \| 6\| B \| The size of each subject within the dataset \| Subject \| Test Length \| Eval Length \| \|---\|---\|---\| \| professional_law \| 1534 \| 5 \| \| moral_scenarios \| 895 \| 5 \| \| miscellaneous \| 783 \| 5 \| \| professional_psychology \| 612 \| 5 \| \| high_school_psychology \| 545 \| 5 \| \| high_school_macroeconomics \| 390 \| 5 \| \| elementary_mathematics \| 378 \| 5 \| \| moral_disputes \| 346 \| 5 \| \| prehistory \| 324 \| 5 \| \| philosophy \| 311 \| 5 \| \| high_school_biology \| 310 \| 5 \| \| nutrition \| 306 \| 5 \| \| professional_accounting \| 282 \| 5 \| \| professional_medicine \| 272 \| 5 \| \| high_school_mathematics \| 270 \| 5 \| \| clinical_knowledge \| 265 \| 5 \| \| security_studies \| 245 \| 5 \| \| high_school_microeconomics \| 238 \| 5 \| \| high_school_world_history \| 237 \| 5 \| \| conceptual_physics \| 235 \| 5 \| \| marketing \| 234 \| 5 \| \| human_aging \| 223 \| 5 \| \| high_school_statistics \| 216 \| 5 \| \| high_school_us_history \| 204 \| 5 \| \| high_school_chemistry \| 203 \| 5 \| \| sociology \| 201 \| 5 \| \| high_school_geography \| 198 \| 5 \| \| high_school_government_and_politics \| 193 \| 5 \| \| college_medicine \| 173 \| 5 \| \| world_religions \| 171 \| 5 \| \| virology \| 166 \| 5 \| \| high_school_european_history \| 165 \| 5 \| \| logical_fallacies \| 163 \| 5 \| \| astronomy \| 152 \| 5 \| \| high_school_physics \| 151 \| 5 \| \| electrical_engineering \| 145 \| 5 \| \| college_biology \| 144 \| 5 \| \| anatomy \| 135 \| 5 \| \| human_sexuality \| 131 \| 5 \| \| formal_logic \| 126 \| 5 \| \| international_law \| 121 \| 5 \| \| econometrics \| 114 \| 5 \| \| machine_learning \| 112 \| 5 \| \| public_relations \| 110 \| 5 \| \| jurisprudence \| 108 \| 5 \| \| management \| 103 \| 5 \| \| college_physics \| 102 \| 5 \| \| abstract_algebra \| 100 \| 5 \| \| business_ethics \| 100 \| 5 \| \| college_chemistry \| 100 \| 5 \| \| college_computer_science \| 100 \| 5 \| \| college_mathematics \| 100 \| 5 \| \| computer_security \| 100 \| 5 \| \| global_facts \| 100 \| 5 \| \| high_school_computer_science \| 100 \| 5 \| \| medical_genetics \| 100 \| 5 \| \| us_foreign_policy \| 100 \| 5 \| \| count \| 14042 \| 285 \|
nlp-waseda/JMMLU	nlp-waseda	"2024-02-27T05:22:30"	514,934	7	[ "task_categories:multiple-choice", "task_categories:question-answering", "language:ja", "license:cc-by-nc-nd-4.0", "size_categories:1K<n<10K", "arxiv:2009.03300", "region:us", "llm", "evaluation", "Japanese" ]	[ "multiple-choice", "question-answering" ]	"2024-02-09T12:19:13"	--- license: cc-by-nc-nd-4.0 task_categories: - multiple-choice - question-answering language: - ja tags: - llm - evaluation - Japanese pretty_name: JMMLU size_categories: - 1K<n<10K --- # JMMLU Japanese Massive Multitask Language Understanding Benchmark JMMLU is a four-choice question set consisting of Japanese-translated questions of a portion of MMLU ([Paper](https://arxiv.org/abs/2009.03300), [Github](https://github.com/hendrycks/test)) (Translated questions) and questions based on unique Japanese cultural context (Japanese questions). It is designed to assess the performance of large language models in Japanese. For the translated questions, a maximum of 150 questions from each of the 57 MMLU tasks (subjects) were selected and first machine-translated into Japanese. Next, the translators checked the machine translations and removed questions and tasks that were difficult to translate, irrelevant, or inconsistent with the Japanese culture. The remaining questions were modified to make them fluent. The Japanese questions are based on school subjects, such as Japanese civics and history, and are manually created by Japanese teachers. The format is the same as MMLU: ``` Question, Choice A, Choice B, Choice C, Choice D, Answer ``` [Github](https://github.com/nlp-waseda/JMMLU) The JMMLU consists of 7,536 questions in the following 56 tasks (subjects). \| Japanese Task Name \| English Task Name \| Number \| \|---\|---\|---:\| \| 専門医学 \| professional_medicine \| 150 \| \| 専門心理学 \| professional_psychology \| 150 \| \| 専門会計 \| professional_accounting \| 150 \| \| 哲学 \| philosophy \| 150 \| \| 雑学 \| miscellaneous \| 150 \| \| 医学遺伝学 \| medical_genetics \| 99 \| \| 形式論理 \| formal_logic \| 125 \| \| 先史学 \| prehistory \| 150 \| \| 天文学 \| astronomy \| 148 \| \| 熟語 \| japanese_idiom \| 150 \| \| 世界宗教 \| world_religions \| 147 \| \| 世界事実 \| global_facts \| 97 \| \| 世界史 \| world_history \| 150 \| \| 社会学 \| sociology \| 150 \| \| 栄養学 \| nutrition \| 149 \| \| 日本史 \| japanese_history \| 150 \| \| 日本地理 \| japanese_geography \| 139 \| \| 人間の老化 \| human_aging \| 150 \| \| 論理学 \| logical_fallacies \| 150 \| \| 倫理的議論 \| moral_disputes \| 148 \| \| 臨床知識 \| clinical_knowledge \| 150 \| \| 経営学 \| management \| 102 \| \| 解剖学 \| anatomy \| 132 \| \| 計量経済学 \| econometrics \| 113 \| \| 機械学習 \| machine_learning \| 111 \| \| 国際法 \| international_law \| 120 \| \| 公民 \| japanese_civics \| 150 \| \| 公共関係 \| public_relations \| 109 \| \| 高校心理学 \| high_school_psychology \| 150 \| \| 高校物理 \| high_school_physics \| 150 \| \| 高校統計学 \| high_school_statistics \| 150 \| \| 高校数学 \| high_school_mathematics \| 150 \| \| 高校生物学 \| high_school_biology \| 148 \| \| 高校情報科学 \| high_school_computer_science \| 98 \| \| 高校化学 \| high_school_chemistry \| 149 \| \| 高校地理 \| high_school_geography \| 150 \| \| 高校ヨーロッパ史 \| high_school_european_history \| 150 \| \| 高校ミクロ経済学 \| high_school_microeconomics \| 149 \| \| 高校マクロ経済学 \| high_school_macroeconomics \| 148 \| \| 概念物理学 \| conceptual_physics \| 150 \| \| 法理学 \| jurisprudence \| 107 \| \| 電気工学 \| electrical_engineering \| 144 \| \| 大学医学 \| college_medicine \| 150 \| \| 大学物理 \| college_physics \| 100 \| \| 大学数学 \| college_mathematics \| 99 \| \| 大学生物学 \| college_biology \| 143 \| \| 大学化学 \| college_chemistry \| 99 \| \| 大学コンピュータ科学 \| college_computer_science \| 99 \| \| 初等数学 \| elementary_mathematics \| 150 \| \| 抽象代数 \| abstract_algebra \| 99 \| \| マーケティング \| marketing \| 150 \| \| ビジネス倫理 \| business_ethics \| 86 \| \| セクシュアリティ \| human_sexuality \| 130 \| \| セキュリティ研究 \| security_studies \| 150 \| \| コンピュータセキュリティ \| computer_security \| 99 \| \| ウイルス学 \| virology \| 150 \| The copyrights for Japanese and World History belongs to STEP Corporation. Commercial use other than for research and evaluation of language models is prohibited. The copyrights for Japanese idioms, Japansese civics, and Japanese geography belong to New Style Cram School VIST. Commercial use is allowed only for research and evaluation of language models. This work is licensed under CC BY-NC-ND 4.0 # Acknowledgment We express our gratitude to the RIKEN for their support in the translation of MMLU. We also acknowledge the contributions from Step Corporation, who provided materials on Japanese and World History, and from New Style Cram School VIST, who supplied resources on japanese_idioms, japansese_civics, and japanese_geography.
lmms-lab/MMMU	lmms-lab	"2024-03-08T05:09:42"	483,639	4	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	null	"2024-01-15T06:32:16"	--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 57719107.0 num_examples: 150 - name: validation num_bytes: 347519954.0 num_examples: 900 - name: test num_bytes: 3271046267.0 num_examples: 10500 download_size: 3377778136 dataset_size: 3676285328.0 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: validation path: data/validation-* - split: test path: data/test-* --- This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated. <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) \| 📚 [Documentation](docs/README.md) \| 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [MMMU](https://github.com/MMMU-Benchmark/MMMU). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{yue2023mmmu, title={Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi}, author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Stevens, Samuel and Jiang, Dongfu and Ren, Weiming and Sun, Yuxuan and others}, journal={arXiv preprint arXiv:2311.16502}, year={2023} } ```
allenai/ai2_arc	allenai	"2023-12-21T15:09:48"	449,088	134	[ "task_categories:question-answering", "task_ids:open-domain-qa", "task_ids:multiple-choice-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1803.05457", "region:us" ]	[ "question-answering" ]	"2022-03-02T23:29:22"	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa - multiple-choice-qa pretty_name: Ai2Arc language_bcp47: - en-US dataset_info: - config_name: ARC-Challenge features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 349760 num_examples: 1119 - name: test num_bytes: 375511 num_examples: 1172 - name: validation num_bytes: 96660 num_examples: 299 download_size: 449460 dataset_size: 821931 - config_name: ARC-Easy features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 619000 num_examples: 2251 - name: test num_bytes: 657514 num_examples: 2376 - name: validation num_bytes: 157394 num_examples: 570 download_size: 762935 dataset_size: 1433908 configs: - config_name: ARC-Challenge data_files: - split: train path: ARC-Challenge/train-* - split: test path: ARC-Challenge/test-* - split: validation path: ARC-Challenge/validation-* - config_name: ARC-Easy data_files: - split: train path: ARC-Easy/train-* - split: test path: ARC-Easy/test-* - split: validation path: ARC-Easy/validation-* --- # Dataset Card for "ai2_arc" ## 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://allenai.org/data/arc](https://allenai.org/data/arc) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1361.68 MB - Size of the generated dataset: 2.28 MB - Total amount of disk used: 1363.96 MB ### Dataset Summary A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### ARC-Challenge - Size of downloaded dataset files: 680.84 MB - Size of the generated dataset: 0.83 MB - Total amount of disk used: 681.67 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` #### ARC-Easy - Size of downloaded dataset files: 680.84 MB - Size of the generated dataset: 1.45 MB - Total amount of disk used: 682.29 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` ### Data Fields The data fields are the same among all splits. #### ARC-Challenge - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. #### ARC-Easy - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-------------\|----:\|---------:\|---:\| \|ARC-Challenge\| 1119\| 299\|1172\| \|ARC-Easy \| 2251\| 570\|2376\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{allenai:arc, author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, journal = {arXiv:1803.05457v1}, year = {2018}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

hails/mmlu_no_train

hails

"2024-01-22T20:46:30"

29,594,679

24

[ "task_categories:question-answering", "language:en", "license:mit", "region:us" ]

[ "question-answering" ]

"2023-10-31T17:25:54"

--- language: - en license: mit task_categories: - question-answering pretty_name: MMLU loader with no auxiliary train set dataset_info: config_name: all features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 6967453 num_examples: 14042 - name: validation num_bytes: 763484 num_examples: 1531 - name: dev num_bytes: 125353 num_examples: 285 download_size: 3987384 dataset_size: 7856290 configs: - config_name: all data_files: - split: test path: all/test-* - split: validation path: all/validation-* - split: dev path: all/dev-* --- This dataset contains a copy of the `cais/mmlu` HF dataset but without the `auxiliary_train` split that takes a long time to generate again each time when loading multiple subsets of the dataset. Please visit https://huggingface.co/datasets/cais/mmlu for more information on the MMLU dataset.

lighteval/mmlu

lighteval

"2023-06-09T16:36:19"

3,665,175

35

[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:1M<n<10M", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2009.03300", "arxiv:2005.00700", "arxiv:2005.14165", "arxiv:2008.02275", "region:us" ]

[ "question-answering" ]

"2023-05-16T09:39:28"

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: mmlu pretty_name: Measuring Massive Multitask Language Understanding language_bcp47: - en-US dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 19328 num_examples: 100 - name: validation num_bytes: 2024 num_examples: 11 - name: dev num_bytes: 830 num_examples: 5 download_size: 166184960 dataset_size: 160623559 - config_name: anatomy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33121 num_examples: 135 - name: validation num_bytes: 3140 num_examples: 14 - name: dev num_bytes: 967 num_examples: 5 download_size: 166184960 dataset_size: 160638605 - config_name: astronomy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46771 num_examples: 152 - name: validation num_bytes: 5027 num_examples: 16 - name: dev num_bytes: 2076 num_examples: 5 download_size: 166184960 dataset_size: 160655251 - config_name: business_ethics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33252 num_examples: 100 - name: validation num_bytes: 3038 num_examples: 11 - name: dev num_bytes: 2190 num_examples: 5 download_size: 166184960 dataset_size: 160639857 - config_name: clinical_knowledge features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 62754 num_examples: 265 - name: validation num_bytes: 6664 num_examples: 29 - name: dev num_bytes: 1210 num_examples: 5 download_size: 166184960 dataset_size: 160672005 - config_name: college_biology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 48797 num_examples: 144 - name: validation num_bytes: 4819 num_examples: 16 - name: dev num_bytes: 1532 num_examples: 5 download_size: 166184960 dataset_size: 160656525 - config_name: college_chemistry features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 24708 num_examples: 100 - name: validation num_bytes: 2328 num_examples: 8 - name: dev num_bytes: 1331 num_examples: 5 download_size: 166184960 dataset_size: 160629744 - config_name: college_computer_science features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 42641 num_examples: 100 - name: validation num_bytes: 4663 num_examples: 11 - name: dev num_bytes: 2765 num_examples: 5 download_size: 166184960 dataset_size: 160651446 - config_name: college_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 24711 num_examples: 100 - name: validation num_bytes: 2668 num_examples: 11 - name: dev num_bytes: 1493 num_examples: 5 download_size: 166184960 dataset_size: 160630249 - config_name: college_medicine features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 82397 num_examples: 173 - name: validation num_bytes: 7909 num_examples: 22 - name: dev num_bytes: 1670 num_examples: 5 download_size: 166184960 dataset_size: 160693353 - config_name: college_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 30181 num_examples: 102 - name: validation num_bytes: 3490 num_examples: 11 - name: dev num_bytes: 1412 num_examples: 5 download_size: 166184960 dataset_size: 160636460 - config_name: computer_security features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 27124 num_examples: 100 - name: validation num_bytes: 4549 num_examples: 11 - name: dev num_bytes: 1101 num_examples: 5 download_size: 166184960 dataset_size: 160634151 - config_name: conceptual_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 40709 num_examples: 235 - name: validation num_bytes: 4474 num_examples: 26 - name: dev num_bytes: 934 num_examples: 5 download_size: 166184960 dataset_size: 160647494 - config_name: econometrics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46547 num_examples: 114 - name: validation num_bytes: 4967 num_examples: 12 - name: dev num_bytes: 1644 num_examples: 5 download_size: 166184960 dataset_size: 160654535 - config_name: electrical_engineering features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 25142 num_examples: 145 - name: validation num_bytes: 2903 num_examples: 16 - name: dev num_bytes: 972 num_examples: 5 download_size: 166184960 dataset_size: 160630394 - config_name: elementary_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 70108 num_examples: 378 - name: validation num_bytes: 8988 num_examples: 41 - name: dev num_bytes: 1440 num_examples: 5 download_size: 166184960 dataset_size: 160681913 - config_name: formal_logic features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 49785 num_examples: 126 - name: validation num_bytes: 6252 num_examples: 14 - name: dev num_bytes: 1757 num_examples: 5 download_size: 166184960 dataset_size: 160659171 - config_name: global_facts features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 18403 num_examples: 100 - name: validation num_bytes: 1865 num_examples: 10 - name: dev num_bytes: 1229 num_examples: 5 download_size: 166184960 dataset_size: 160622874 - config_name: high_school_biology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 109732 num_examples: 310 - name: validation num_bytes: 11022 num_examples: 32 - name: dev num_bytes: 1673 num_examples: 5 download_size: 166184960 dataset_size: 160723804 - config_name: high_school_chemistry features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 58464 num_examples: 203 - name: validation num_bytes: 7092 num_examples: 22 - name: dev num_bytes: 1220 num_examples: 5 download_size: 166184960 dataset_size: 160668153 - config_name: high_school_computer_science features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 44476 num_examples: 100 - name: validation num_bytes: 3343 num_examples: 9 - name: dev num_bytes: 2918 num_examples: 5 download_size: 166184960 dataset_size: 160652114 - config_name: high_school_european_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 270300 num_examples: 165 - name: validation num_bytes: 29632 num_examples: 18 - name: dev num_bytes: 11564 num_examples: 5 download_size: 166184960 dataset_size: 160912873 - config_name: high_school_geography features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 42034 num_examples: 198 - name: validation num_bytes: 4332 num_examples: 22 - name: dev num_bytes: 1403 num_examples: 5 download_size: 166184960 dataset_size: 160649146 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 66074 num_examples: 193 - name: validation num_bytes: 7063 num_examples: 21 - name: dev num_bytes: 1779 num_examples: 5 download_size: 166184960 dataset_size: 160676293 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 117687 num_examples: 390 - name: validation num_bytes: 13020 num_examples: 43 - name: dev num_bytes: 1328 num_examples: 5 download_size: 166184960 dataset_size: 160733412 - config_name: high_school_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 54854 num_examples: 270 - name: validation num_bytes: 5765 num_examples: 29 - name: dev num_bytes: 1297 num_examples: 5 download_size: 166184960 dataset_size: 160663293 - config_name: high_school_microeconomics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 75703 num_examples: 238 - name: validation num_bytes: 7553 num_examples: 26 - name: dev num_bytes: 1298 num_examples: 5 download_size: 166184960 dataset_size: 160685931 - config_name: high_school_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 59538 num_examples: 151 - name: validation num_bytes: 6771 num_examples: 17 - name: dev num_bytes: 1489 num_examples: 5 download_size: 166184960 dataset_size: 160669175 - config_name: high_school_psychology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 159407 num_examples: 545 - name: validation num_bytes: 17269 num_examples: 60 - name: dev num_bytes: 1905 num_examples: 5 download_size: 166184960 dataset_size: 160779958 - config_name: high_school_statistics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 110702 num_examples: 216 - name: validation num_bytes: 9997 num_examples: 23 - name: dev num_bytes: 2528 num_examples: 5 download_size: 166184960 dataset_size: 160724604 - config_name: high_school_us_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 296734 num_examples: 204 - name: validation num_bytes: 31706 num_examples: 22 - name: dev num_bytes: 8864 num_examples: 5 download_size: 166184960 dataset_size: 160938681 - config_name: high_school_world_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 378617 num_examples: 237 - name: validation num_bytes: 45501 num_examples: 26 - name: dev num_bytes: 4882 num_examples: 5 download_size: 166184960 dataset_size: 161030377 - config_name: human_aging features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46098 num_examples: 223 - name: validation num_bytes: 4707 num_examples: 23 - name: dev num_bytes: 1008 num_examples: 5 download_size: 166184960 dataset_size: 160653190 - config_name: human_sexuality features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 32110 num_examples: 131 - name: validation num_bytes: 2421 num_examples: 12 - name: dev num_bytes: 1077 num_examples: 5 download_size: 166184960 dataset_size: 160636985 - config_name: international_law features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 53531 num_examples: 121 - name: validation num_bytes: 6473 num_examples: 13 - name: dev num_bytes: 2418 num_examples: 5 download_size: 166184960 dataset_size: 160663799 - config_name: jurisprudence features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33986 num_examples: 108 - name: validation num_bytes: 3729 num_examples: 11 - name: dev num_bytes: 1303 num_examples: 5 download_size: 166184960 dataset_size: 160640395 - config_name: logical_fallacies features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 50117 num_examples: 163 - name: validation num_bytes: 5103 num_examples: 18 - name: dev num_bytes: 1573 num_examples: 5 download_size: 166184960 dataset_size: 160658170 - config_name: machine_learning features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33880 num_examples: 112 - name: validation num_bytes: 3232 num_examples: 11 - name: dev num_bytes: 2323 num_examples: 5 download_size: 166184960 dataset_size: 160640812 - config_name: management features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 20002 num_examples: 103 - name: validation num_bytes: 1820 num_examples: 11 - name: dev num_bytes: 898 num_examples: 5 download_size: 166184960 dataset_size: 160624097 - config_name: marketing features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 63025 num_examples: 234 - name: validation num_bytes: 7394 num_examples: 25 - name: dev num_bytes: 1481 num_examples: 5 download_size: 166184960 dataset_size: 160673277 - config_name: medical_genetics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 20864 num_examples: 100 - name: validation num_bytes: 3005 num_examples: 11 - name: dev num_bytes: 1089 num_examples: 5 download_size: 166184960 dataset_size: 160626335 - config_name: miscellaneous features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 147704 num_examples: 783 - name: validation num_bytes: 14330 num_examples: 86 - name: dev num_bytes: 699 num_examples: 5 download_size: 166184960 dataset_size: 160764110 - config_name: moral_disputes features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 107818 num_examples: 346 - name: validation num_bytes: 12420 num_examples: 38 - name: dev num_bytes: 1755 num_examples: 5 download_size: 166184960 dataset_size: 160723370 - config_name: moral_scenarios features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 374026 num_examples: 895 - name: validation num_bytes: 42338 num_examples: 100 - name: dev num_bytes: 2058 num_examples: 5 download_size: 166184960 dataset_size: 161019799 - config_name: nutrition features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 92410 num_examples: 306 - name: validation num_bytes: 8436 num_examples: 33 - name: dev num_bytes: 2085 num_examples: 5 download_size: 166184960 dataset_size: 160704308 - config_name: philosophy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 80073 num_examples: 311 - name: validation num_bytes: 9184 num_examples: 34 - name: dev num_bytes: 988 num_examples: 5 download_size: 166184960 dataset_size: 160691622 - config_name: prehistory features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 89594 num_examples: 324 - name: validation num_bytes: 10285 num_examples: 35 - name: dev num_bytes: 1878 num_examples: 5 download_size: 166184960 dataset_size: 160703134 - config_name: professional_accounting features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 124550 num_examples: 282 - name: validation num_bytes: 14372 num_examples: 31 - name: dev num_bytes: 2148 num_examples: 5 download_size: 166184960 dataset_size: 160742447 - config_name: professional_law features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 1891762 num_examples: 1534 - name: validation num_bytes: 203519 num_examples: 170 - name: dev num_bytes: 6610 num_examples: 5 download_size: 166184960 dataset_size: 162703268 - config_name: professional_medicine features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 217561 num_examples: 272 - name: validation num_bytes: 23847 num_examples: 31 - name: dev num_bytes: 3807 num_examples: 5 download_size: 166184960 dataset_size: 160846592 - config_name: professional_psychology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 225899 num_examples: 612 - name: validation num_bytes: 29101 num_examples: 69 - name: dev num_bytes: 2267 num_examples: 5 download_size: 166184960 dataset_size: 160858644 - config_name: public_relations features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 28760 num_examples: 110 - name: validation num_bytes: 4566 num_examples: 12 - name: dev num_bytes: 1496 num_examples: 5 download_size: 166184960 dataset_size: 160636199 - config_name: security_studies features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 204844 num_examples: 245 - name: validation num_bytes: 22637 num_examples: 27 - name: dev num_bytes: 5335 num_examples: 5 download_size: 166184960 dataset_size: 160834193 - config_name: sociology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 66243 num_examples: 201 - name: validation num_bytes: 7184 num_examples: 22 - name: dev num_bytes: 1613 num_examples: 5 download_size: 166184960 dataset_size: 160676417 - config_name: us_foreign_policy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 28443 num_examples: 100 - name: validation num_bytes: 3264 num_examples: 11 - name: dev num_bytes: 1611 num_examples: 5 download_size: 166184960 dataset_size: 160634695 - config_name: virology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 38759 num_examples: 166 - name: validation num_bytes: 5463 num_examples: 18 - name: dev num_bytes: 1096 num_examples: 5 download_size: 166184960 dataset_size: 160646695 - config_name: world_religions features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 25274 num_examples: 171 - name: validation num_bytes: 2765 num_examples: 19 - name: dev num_bytes: 670 num_examples: 5 download_size: 166184960 dataset_size: 160630086 --- # Dataset Card for MMLU ## 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 - **Repository**: https://github.com/hendrycks/test - **Paper**: https://arxiv.org/abs/2009.03300 ### Dataset Summary [Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions'] ### Supported Tasks and Leaderboards | Model | Authors | Humanities | Social Science | STEM | Other | Average | |------------------------------------|----------|:-------:|:-------:|:-------:|:-------:|:-------:| | [UnifiedQA](https://arxiv.org/abs/2005.00700) | Khashabi et al., 2020 | 45.6 | 56.6 | 40.2 | 54.6 | 48.9 | [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) | Brown et al., 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9 | [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al., 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4 | Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 ### Languages English ## Dataset Structure ### Data Instances An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"], "answer": "D" } ``` ### Data Fields - `question`: a string feature - `choices`: a list of 4 string features - `answer`: a ClassLabel feature ### Data Splits - `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc. - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask | | auxiliary_train | dev | val | test | | ----- | :------: | :-----: | :-----: | :-----: | | TOTAL | 99842 | 285 | 1531 | 14042 ## Dataset Creation ### Curation Rationale Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn. ### 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 [MIT License](https://github.com/hendrycks/test/blob/master/LICENSE) ### Citation Information If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from: ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ``` ### Contributions Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.

SaylorTwift/bbh

SaylorTwift

"2024-06-16T12:12:34"

2,589,462

2

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

null

"2024-06-12T15:26:17"

--- dataset_info: - config_name: boolean_expressions features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 11790 num_examples: 250 download_size: 4700 dataset_size: 11790 - config_name: causal_judgement features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 198021 num_examples: 187 download_size: 69494 dataset_size: 198021 - config_name: date_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 54666 num_examples: 250 download_size: 18041 dataset_size: 54666 - config_name: default features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 50971 num_examples: 250 download_size: 21723 dataset_size: 50971 - config_name: disambiguation_qa features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 78620 num_examples: 250 download_size: 16704 dataset_size: 78620 - config_name: dyck_languages features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38432 num_examples: 250 download_size: 10015 dataset_size: 38432 - config_name: formal_fallacies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 138224 num_examples: 250 download_size: 35789 dataset_size: 138224 - config_name: geometric_shapes features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 68560 num_examples: 250 download_size: 20233 dataset_size: 68560 - config_name: hyperbaton features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38574 num_examples: 250 download_size: 10422 dataset_size: 38574 - config_name: logical_deduction_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 148595 num_examples: 250 download_size: 33498 dataset_size: 148595 - config_name: logical_deduction_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 191022 num_examples: 250 download_size: 43970 dataset_size: 191022 - config_name: logical_deduction_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 105831 num_examples: 250 download_size: 21597 dataset_size: 105831 - config_name: movie_recommendation features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 50971 num_examples: 250 download_size: 21723 dataset_size: 50971 - config_name: multistep_arithmetic_two features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 12943 num_examples: 250 download_size: 7552 dataset_size: 12943 - config_name: navigate features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 49031 num_examples: 250 download_size: 10032 dataset_size: 49031 - config_name: object_counting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 30508 num_examples: 250 download_size: 10586 dataset_size: 30508 - config_name: penguins_in_a_table features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 70062 num_examples: 146 download_size: 10654 dataset_size: 70062 - config_name: reasoning_about_colored_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 89579 num_examples: 250 download_size: 20387 dataset_size: 89579 - config_name: ruin_names features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 46469 num_examples: 250 download_size: 15475 dataset_size: 46469 - config_name: salient_translation_error_detection features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 277110 num_examples: 250 download_size: 56862 dataset_size: 277110 - config_name: snarks features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38223 num_examples: 178 download_size: 16406 dataset_size: 38223 - config_name: sports_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 22723 num_examples: 250 download_size: 8163 dataset_size: 22723 - config_name: temporal_sequences features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 139546 num_examples: 250 download_size: 35571 dataset_size: 139546 - config_name: tracking_shuffled_objects_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 162590 num_examples: 250 download_size: 37111 dataset_size: 162590 - config_name: tracking_shuffled_objects_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 207274 num_examples: 250 download_size: 49062 dataset_size: 207274 - config_name: tracking_shuffled_objects_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 122104 num_examples: 250 download_size: 25142 dataset_size: 122104 - config_name: web_of_lies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 47582 num_examples: 250 download_size: 15615 dataset_size: 47582 - config_name: word_sorting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 60918 num_examples: 250 download_size: 44584 dataset_size: 60918 configs: - config_name: boolean_expressions data_files: - split: test path: boolean_expressions/test-* - config_name: causal_judgement data_files: - split: test path: causal_judgement/test-* - config_name: date_understanding data_files: - split: test path: date_understanding/test-* - config_name: default data_files: - split: test path: data/test-* - config_name: disambiguation_qa data_files: - split: test path: disambiguation_qa/test-* - config_name: dyck_languages data_files: - split: test path: dyck_languages/test-* - config_name: formal_fallacies data_files: - split: test path: formal_fallacies/test-* - config_name: geometric_shapes data_files: - split: test path: geometric_shapes/test-* - config_name: hyperbaton data_files: - split: test path: hyperbaton/test-* - config_name: logical_deduction_five_objects data_files: - split: test path: logical_deduction_five_objects/test-* - config_name: logical_deduction_seven_objects data_files: - split: test path: logical_deduction_seven_objects/test-* - config_name: logical_deduction_three_objects data_files: - split: test path: logical_deduction_three_objects/test-* - config_name: movie_recommendation data_files: - split: test path: movie_recommendation/test-* - config_name: multistep_arithmetic_two data_files: - split: test path: multistep_arithmetic_two/test-* - config_name: navigate data_files: - split: test path: navigate/test-* - config_name: object_counting data_files: - split: test path: object_counting/test-* - config_name: penguins_in_a_table data_files: - split: test path: penguins_in_a_table/test-* - config_name: reasoning_about_colored_objects data_files: - split: test path: reasoning_about_colored_objects/test-* - config_name: ruin_names data_files: - split: test path: ruin_names/test-* - config_name: salient_translation_error_detection data_files: - split: test path: salient_translation_error_detection/test-* - config_name: snarks data_files: - split: test path: snarks/test-* - config_name: sports_understanding data_files: - split: test path: sports_understanding/test-* - config_name: temporal_sequences data_files: - split: test path: temporal_sequences/test-* - config_name: tracking_shuffled_objects_five_objects data_files: - split: test path: tracking_shuffled_objects_five_objects/test-* - config_name: tracking_shuffled_objects_seven_objects data_files: - split: test path: tracking_shuffled_objects_seven_objects/test-* - config_name: tracking_shuffled_objects_three_objects data_files: - split: test path: tracking_shuffled_objects_three_objects/test-* - config_name: web_of_lies data_files: - split: test path: web_of_lies/test-* - config_name: word_sorting data_files: - split: test path: word_sorting/test-* ---

argilla/databricks-dolly-15k-curated-en

argilla

"2023-10-02T12:32:53"

1,972,100

44

[ "language:en", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

null

"2023-05-30T09:54:44"

--- language: - en --- ## Guidelines In this dataset, you will find a collection of records that show a category, an instruction, a context and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible. To curate the dataset, you will need to provide an answer to the following text fields: 1 - Final instruction: The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record. 2 - Final context: The final version of the instruction field. You may copy it using the copy icon in the context field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an context to be completed, leave this question blank. 3 - Final response: The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above. You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard. ## Fields * `id` is of type <class 'str'> * `category` is of type <class 'str'> * `original-instruction` is of type <class 'str'> * `original-context` is of type <class 'str'> * `original-response` is of type <class 'str'> ## Questions * `new-instruction` : Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here. * `new-context` : Write the final version of the context, making sure that it makes sense with the task category. If the original context is ok, copy and paste it here. If an context is not needed, leave this empty. * `new-response` : Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and context) provided. If the original response is ok, copy and paste it here. ## Load with Argilla To load this dataset with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface('argilla/databricks-dolly-15k-curated-en') ``` ## Load with Datasets To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset('argilla/databricks-dolly-15k-curated-en') ```

lavita/medical-qa-shared-task-v1-toy

lavita

"2023-07-20T00:29:06"

1,523,272

17

[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

null

"2023-07-20T00:28:51"

--- dataset_info: features: - name: id dtype: int64 - name: ending0 dtype: string - name: ending1 dtype: string - name: ending2 dtype: string - name: ending3 dtype: string - name: ending4 dtype: string - name: label dtype: int64 - name: sent1 dtype: string - name: sent2 dtype: string - name: startphrase dtype: string splits: - name: train num_bytes: 52480.01886421694 num_examples: 32 - name: dev num_bytes: 52490.64150943396 num_examples: 32 download_size: 89680 dataset_size: 104970.6603736509 --- # Dataset Card for "medical-qa-shared-task-v1-toy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mteb/banking77

mteb

"2022-09-27T19:15:02"

934,717

1

[ "language:en", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

null

"2022-05-17T12:14:06"

--- language: - en ---

ceval/ceval-exam

ceval

"2023-08-31T14:04:10"

826,634

240

[ "task_categories:text-classification", "task_categories:multiple-choice", "task_categories:question-answering", "language:zh", "license:cc-by-nc-sa-4.0", "size_categories:10K<n<100K", "modality:text", "library:datasets", "library:mlcroissant", "arxiv:2305.08322", "region:us" ]

[ "text-classification", "multiple-choice", "question-answering" ]

"2023-05-16T01:47:44"

--- license: cc-by-nc-sa-4.0 task_categories: - text-classification - multiple-choice - question-answering language: - zh pretty_name: C-Eval size_categories: - 10K<n<100K --- C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https://arxiv.org/abs/2305.08322) for more details. Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/SJTU-LIT/ceval/tree/main#how-to-submit) ### Load the data ```python from datasets import load_dataset dataset=load_dataset(r"ceval/ceval-exam",name="computer_network") print(dataset['val'][0]) # {'id': 0, 'question': '使用位填充方法，以01111110为位首flag，数据为011011111111111111110010，求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ''} ``` More details on loading and using the data are at our [github page](https://github.com/SJTU-LIT/ceval#data). Please cite our paper if you use our dataset. ``` @article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, journal={arXiv preprint arXiv:2305.08322}, year={2023} } ```

EleutherAI/hendrycks_math

EleutherAI

"2023-11-02T14:48:57"

737,801

8

[ "license:mit", "region:us" ]

null

"2023-09-14T20:28:56"

--- license: mit ---

HAERAE-HUB/KMMLU

HAERAE-HUB

"2024-03-05T14:13:32"

685,530

51

[ "task_categories:multiple-choice", "language:ko", "license:cc-by-nd-4.0", "size_categories:100K<n<1M", "format:csv", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2402.11548", "region:us", "mmlu", "haerae" ]

[ "multiple-choice" ]

"2023-11-27T09:06:18"

--- configs: - config_name: Accounting data_files: - split: train path: data/Accounting-train.csv - split: dev path: data/Accounting-dev.csv - split: test path: data/Accounting-test.csv - config_name: Agricultural-Sciences data_files: - split: train path: data/Agricultural-Sciences-train.csv - split: dev path: data/Agricultural-Sciences-dev.csv - split: test path: data/Agricultural-Sciences-test.csv - config_name: Aviation-Engineering-and-Maintenance data_files: - split: train path: data/Aviation-Engineering-and-Maintenance-train.csv - split: dev path: data/Aviation-Engineering-and-Maintenance-dev.csv - split: test path: data/Aviation-Engineering-and-Maintenance-test.csv - config_name: Biology data_files: - split: train path: data/Biology-train.csv - split: dev path: data/Biology-dev.csv - split: test path: data/Biology-test.csv - config_name: Chemical-Engineering data_files: - split: train path: data/Chemical-Engineering-train.csv - split: dev path: data/Chemical-Engineering-dev.csv - split: test path: data/Chemical-Engineering-test.csv - config_name: Chemistry data_files: - split: train path: data/Chemistry-train.csv - split: dev path: data/Chemistry-dev.csv - split: test path: data/Chemistry-test.csv - config_name: Civil-Engineering data_files: - split: train path: data/Civil-Engineering-train.csv - split: dev path: data/Civil-Engineering-dev.csv - split: test path: data/Civil-Engineering-test.csv - config_name: Computer-Science data_files: - split: train path: data/Computer-Science-train.csv - split: dev path: data/Computer-Science-dev.csv - split: test path: data/Computer-Science-test.csv - config_name: Construction data_files: - split: train path: data/Construction-train.csv - split: dev path: data/Construction-dev.csv - split: test path: data/Construction-test.csv - config_name: Criminal-Law data_files: - split: train path: data/Criminal-Law-train.csv - split: dev path: data/Criminal-Law-dev.csv - split: test path: data/Criminal-Law-test.csv - config_name: Ecology data_files: - split: train path: data/Ecology-train.csv - split: dev path: data/Ecology-dev.csv - split: test path: data/Ecology-test.csv - config_name: Economics data_files: - split: train path: data/Economics-train.csv - split: dev path: data/Economics-dev.csv - split: test path: data/Economics-test.csv - config_name: Education data_files: - split: train path: data/Education-train.csv - split: dev path: data/Education-dev.csv - split: test path: data/Education-test.csv - config_name: Electrical-Engineering data_files: - split: train path: data/Electrical-Engineering-train.csv - split: dev path: data/Electrical-Engineering-dev.csv - split: test path: data/Electrical-Engineering-test.csv - config_name: Electronics-Engineering data_files: - split: train path: data/Electronics-Engineering-train.csv - split: dev path: data/Electronics-Engineering-dev.csv - split: test path: data/Electronics-Engineering-test.csv - config_name: Energy-Management data_files: - split: train path: data/Energy-Management-train.csv - split: dev path: data/Energy-Management-dev.csv - split: test path: data/Energy-Management-test.csv - config_name: Environmental-Science data_files: - split: train path: data/Environmental-Science-train.csv - split: dev path: data/Environmental-Science-dev.csv - split: test path: data/Environmental-Science-test.csv - config_name: Fashion data_files: - split: train path: data/Fashion-train.csv - split: dev path: data/Fashion-dev.csv - split: test path: data/Fashion-test.csv - config_name: Food-Processing data_files: - split: train path: data/Food-Processing-train.csv - split: dev path: data/Food-Processing-dev.csv - split: test path: data/Food-Processing-test.csv - config_name: Gas-Technology-and-Engineering data_files: - split: train path: data/Gas-Technology-and-Engineering-train.csv - split: dev path: data/Gas-Technology-and-Engineering-dev.csv - split: test path: data/Gas-Technology-and-Engineering-test.csv - config_name: Geomatics data_files: - split: train path: data/Geomatics-train.csv - split: dev path: data/Geomatics-dev.csv - split: test path: data/Geomatics-test.csv - config_name: Health data_files: - split: train path: data/Health-train.csv - split: dev path: data/Health-dev.csv - split: test path: data/Health-test.csv - config_name: Industrial-Engineer data_files: - split: train path: data/Industrial-Engineer-train.csv - split: dev path: data/Industrial-Engineer-dev.csv - split: test path: data/Industrial-Engineer-test.csv - config_name: Information-Technology data_files: - split: train path: data/Information-Technology-train.csv - split: dev path: data/Information-Technology-dev.csv - split: test path: data/Information-Technology-test.csv - config_name: Interior-Architecture-and-Design data_files: - split: train path: data/Interior-Architecture-and-Design-train.csv - split: dev path: data/Interior-Architecture-and-Design-dev.csv - split: test path: data/Interior-Architecture-and-Design-test.csv - config_name: Law data_files: - split: train path: data/Law-train.csv - split: dev path: data/Law-dev.csv - split: test path: data/Law-test.csv - config_name: Machine-Design-and-Manufacturing data_files: - split: train path: data/Machine-Design-and-Manufacturing-train.csv - split: dev path: data/Machine-Design-and-Manufacturing-dev.csv - split: test path: data/Machine-Design-and-Manufacturing-test.csv - config_name: Management data_files: - split: train path: data/Management-train.csv - split: dev path: data/Management-dev.csv - split: test path: data/Management-test.csv - config_name: Maritime-Engineering data_files: - split: train path: data/Maritime-Engineering-train.csv - split: dev path: data/Maritime-Engineering-dev.csv - split: test path: data/Maritime-Engineering-test.csv - config_name: Marketing data_files: - split: train path: data/Marketing-train.csv - split: dev path: data/Marketing-dev.csv - split: test path: data/Marketing-test.csv - config_name: Materials-Engineering data_files: - split: train path: data/Materials-Engineering-train.csv - split: dev path: data/Materials-Engineering-dev.csv - split: test path: data/Materials-Engineering-test.csv - config_name: Mechanical-Engineering data_files: - split: train path: data/Mechanical-Engineering-train.csv - split: dev path: data/Mechanical-Engineering-dev.csv - split: test path: data/Mechanical-Engineering-test.csv - config_name: Nondestructive-Testing data_files: - split: train path: data/Nondestructive-Testing-train.csv - split: dev path: data/Nondestructive-Testing-dev.csv - split: test path: data/Nondestructive-Testing-test.csv - config_name: Patent data_files: - split: train path: data/Patent-train.csv - split: dev path: data/Patent-dev.csv - split: test path: data/Patent-test.csv - config_name: Political-Science-and-Sociology data_files: - split: train path: data/Political-Science-and-Sociology-train.csv - split: dev path: data/Political-Science-and-Sociology-dev.csv - split: test path: data/Political-Science-and-Sociology-test.csv - config_name: Psychology data_files: - split: train path: data/Psychology-train.csv - split: dev path: data/Psychology-dev.csv - split: test path: data/Psychology-test.csv - config_name: Public-Safety data_files: - split: train path: data/Public-Safety-train.csv - split: dev path: data/Public-Safety-dev.csv - split: test path: data/Public-Safety-test.csv - config_name: Railway-and-Automotive-Engineering data_files: - split: train path: data/Railway-and-Automotive-Engineering-train.csv - split: dev path: data/Railway-and-Automotive-Engineering-dev.csv - split: test path: data/Railway-and-Automotive-Engineering-test.csv - config_name: Real-Estate data_files: - split: train path: data/Real-Estate-train.csv - split: dev path: data/Real-Estate-dev.csv - split: test path: data/Real-Estate-test.csv - config_name: Refrigerating-Machinery data_files: - split: train path: data/Refrigerating-Machinery-train.csv - split: dev path: data/Refrigerating-Machinery-dev.csv - split: test path: data/Refrigerating-Machinery-test.csv - config_name: Social-Welfare data_files: - split: train path: data/Social-Welfare-train.csv - split: dev path: data/Social-Welfare-dev.csv - split: test path: data/Social-Welfare-test.csv - config_name: Taxation data_files: - split: train path: data/Taxation-train.csv - split: dev path: data/Taxation-dev.csv - split: test path: data/Taxation-test.csv - config_name: Telecommunications-and-Wireless-Technology data_files: - split: train path: data/Telecommunications-and-Wireless-Technology-train.csv - split: dev path: data/Telecommunications-and-Wireless-Technology-dev.csv - split: test path: data/Telecommunications-and-Wireless-Technology-test.csv - config_name: Korean-History data_files: - split: train path: data/korean-history-train.csv - split: dev path: data/korean-history-dev.csv - split: test path: data/korean-history-test.csv - config_name: Math data_files: - split: train path: data/math-train.csv - split: dev path: data/math-dev.csv - split: test path: data/math-test.csv task_categories: - multiple-choice language: - ko tags: - mmlu - haerae size_categories: - 10K<n<100K license: cc-by-nd-4.0 --- # KMMLU (Korean-MMLU) We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks that are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 26 publically available and proprietary LLMs, identifying significant room for improvement. The best publicly available model achieves 50.54% on KMMLU, far below the average human performance of 62.6%. This model was primarily trained for English and Chinese, not Korean. Current LLMs tailored to Korean, such as Polyglot-Ko, perform far worse. Surprisingly, even the most capable proprietary LLMs, e.g., GPT-4 and HyperCLOVA X, achieve 59.95% and 53.40%, respectively. This suggests that further work is needed to improve Korean LLMs, and KMMLU offers the right tool to track this progress. We make our dataset publicly available on the Hugging Face Hub and integrate the benchmark into EleutherAI's Language Model Evaluation Harness. Link to Paper: [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://arxiv.org/abs/2402.11548) ### KMMLU Statistics | Category | # Questions | |------------------------------|-------------| | **Prerequisites** | | | None | 59,909 | | 1 Prerequisite Test | 12,316 | | 2 Prerequisite Tests | 776 | | 2+ Years of Experience | 65,135 | | 4+ Years of Experience | 98,678 | | 9+ Years of Experience | 6,963 | | **Question Type** | | | Positive | 207,030 | | Negation | 36,777 | | **Split** | | | Train | 208,522 | | Validation | 225 | | Test | 35,030 | | **Total** | 243,777 | ### Categories To reimplement the categories in the paper, refer to the following: ``` supercategories = { "accounting": "HUMSS", "agricultural_sciences": "Other", "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_engineering": "STEM", "electronics_engineering": "Applied Science", "energy_management": "Applied Science", "environmental_science": "Applied Science", "fashion": "Other", "food_processing": "Other", "gas_technology_and_engineering": "Applied Science", "geomatics": "Applied Science", "health": "Other", "industrial_engineer": "Applied Science", "information_technology": "STEM", "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "patent": "Other", "political_science_and_sociology": "HUMSS", "psychology": "HUMSS", "public_safety": "Other", "railway_and_automotive_engineering": "Applied Science", "real_estate": "Other", "refrigerating_machinery": "Other", "social_welfare": "HUMSS", "taxation": "HUMSS", "telecommunications_and_wireless_technology": "Applied Science", "korean_history": "HUMSS", "math": "STEM" } ``` ### Point of Contact For any questions contact us via the following email:) ``` spthsrbwls123@yonsei.ac.kr ```

lighteval/MATH-Hard

lighteval

"2024-06-12T13:00:08"

672,558

13

[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2103.03874", "region:us", "explanation-generation" ]

[ "text2text-generation" ]

"2024-06-12T09:59:43"

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual source_datasets: - original task_categories: - text2text-generation task_ids: [] pretty_name: Mathematics Aptitude Test of Heuristics (MATH) tags: - explanation-generation dataset_info: features: - name: problem dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string configs: - config_name: default data_files: - split: train path: train/* - split: test path: test/* - config_name: algebra data_files: - split: train path: train/algebra.jsonl - split: test path: test/algebra.jsonl - config_name: counting_and_probability data_files: - split: train path: train/counting_and_probability.jsonl - split: test path: test/counting_and_probability.jsonl - config_name: geometry data_files: - split: train path: train/geometry.jsonl - split: test path: test/geometry.jsonl - config_name: intermediate_algebra data_files: - split: train path: train/intermediate_algebra.jsonl - split: test path: test/intermediate_algebra.jsonl - config_name: number_theory data_files: - split: train path: train/number_theory.jsonl - split: test path: test/number_theory.jsonl - config_name: prealgebra data_files: - split: train path: train/prealgebra.jsonl - split: test path: test/prealgebra.jsonl - config_name: precalculus data_files: - split: train path: train/precalculus.jsonl - split: test path: test/precalculus.jsonl --- # Dataset Card for Mathematics Aptitude Test of Heuristics, hard subset (MATH-Hard) dataset ## Dataset Description - **Homepage:** https://github.com/hendrycks/math - **Repository:** https://github.com/hendrycks/math - **Paper:** https://arxiv.org/pdf/2103.03874.pdf - **Leaderboard:** N/A - **Point of Contact:** Dan Hendrycks ### Dataset Summary The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations. For MATH-Hard, only the hardest questions were kept (Level 5). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's `\boxed` tag. An example from the dataset is: ``` {'problem': 'A board game spinner is divided into three parts labeled $A$, $B$ and $C$. The probability of the spinner landing on $A$ is $\\frac{1}{3}$ and the probability of the spinner landing on $B$ is $\\frac{5}{12}$. What is the probability of the spinner landing on $C$? Express your answer as a common fraction.', 'level': 'Level 1', 'type': 'Counting & Probability', 'solution': 'The spinner is guaranteed to land on exactly one of the three regions, so we know that the sum of the probabilities of it landing in each region will be 1. If we let the probability of it landing in region $C$ be $x$, we then have the equation $1 = \\frac{5}{12}+\\frac{1}{3}+x$, from which we have $x=\\boxed{\\frac{1}{4}}$.'} ``` ### Data Fields * `problem`: The competition math problem. * `solution`: The step-by-step solution. * `level`: We only kept tasks tagged as 'Level 5', the hardest level for the dataset. * `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus. ### Licensing Information https://github.com/hendrycks/math/blob/main/LICENSE ### Citation Information ```bibtex @article{hendrycksmath2021, title={Measuring Mathematical Problem Solving With the MATH Dataset}, author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, journal={arXiv preprint arXiv:2103.03874}, year={2021} } ```

cais/mmlu

cais

"2024-03-08T20:36:26"

566,256

311

[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2009.03300", "arxiv:2005.00700", "arxiv:2005.14165", "arxiv:2008.02275", "region:us" ]

[ "question-answering" ]

"2022-03-02T23:29:22"

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: mmlu pretty_name: Measuring Massive Multitask Language Understanding language_bcp47: - en-US dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 17143 dataset_size: 57303.3562203159 - config_name: all features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 6967453 num_examples: 14042 - name: validation num_bytes: 763484 num_examples: 1531 - name: dev num_bytes: 125353 num_examples: 285 - name: auxiliary_train num_bytes: 161000625 num_examples: 99842 download_size: 51503402 dataset_size: 168856915 - config_name: anatomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 66985.19833357072 num_examples: 135 - name: validation num_bytes: 6981.5649902024825 num_examples: 14 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 28864 dataset_size: 76165.9387623697 - config_name: astronomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 75420.3714570574 num_examples: 152 - name: validation num_bytes: 7978.931417374265 num_examples: 16 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 39316 dataset_size: 85598.47831302814 - config_name: auxiliary_train features: - name: train struct: - name: answer dtype: int64 - name: choices sequence: string - name: question dtype: string - name: subject dtype: string splits: - name: train num_bytes: 161000625 num_examples: 99842 download_size: 47518592 dataset_size: 161000625 - config_name: business_ethics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 31619 dataset_size: 57303.3562203159 - config_name: clinical_knowledge features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 131489.4633955277 num_examples: 265 - name: validation num_bytes: 14461.813193990856 num_examples: 29 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 51655 dataset_size: 148150.45202811505 - config_name: college_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 71450.87822247542 num_examples: 144 - name: validation num_bytes: 7978.931417374265 num_examples: 16 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 43017 dataset_size: 81628.98507844617 - config_name: college_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 3989.4657086871325 num_examples: 8 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 26781 dataset_size: 55807.30657955822 - config_name: college_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 41132 dataset_size: 57303.3562203159 - config_name: college_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 26779 dataset_size: 57303.3562203159 - config_name: college_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 85840.29119783506 num_examples: 173 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 56303 dataset_size: 99010.49733532117 - config_name: college_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 50611.0387409201 num_examples: 102 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 29539 dataset_size: 58295.7295289614 - config_name: computer_security features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 30150 dataset_size: 57303.3562203159 - config_name: conceptual_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 116603.86376584532 num_examples: 235 - name: validation num_bytes: 12965.76355323318 num_examples: 26 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 34968 dataset_size: 131768.802757675 - config_name: econometrics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 56565.27859279305 num_examples: 114 - name: validation num_bytes: 5984.198563030699 num_examples: 12 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 36040 dataset_size: 64748.652594420244 - config_name: electrical_engineering features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 71947.06487679818 num_examples: 145 - name: validation num_bytes: 7978.931417374265 num_examples: 16 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 26746 dataset_size: 82125.17173276893 - config_name: elementary_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 187558.555333998 num_examples: 378 - name: validation num_bytes: 20446.011757021555 num_examples: 41 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 54987 dataset_size: 210203.74252961605 - config_name: formal_logic features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 62519.518444666 num_examples: 126 - name: validation num_bytes: 6981.5649902024825 num_examples: 14 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 32884 dataset_size: 71700.25887346498 - config_name: global_facts features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 4986.8321358589155 num_examples: 10 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 19258 dataset_size: 56804.67300673001 - config_name: high_school_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 153817.86284005127 num_examples: 310 - name: validation num_bytes: 15957.86283474853 num_examples: 32 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 78216 dataset_size: 171974.90111339628 - config_name: high_school_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 100725.89082751745 num_examples: 203 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 45799 dataset_size: 113896.09696500355 - config_name: high_school_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 4488.148922273024 num_examples: 9 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 39072 dataset_size: 56305.989793144116 - config_name: high_school_european_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 81870.79796325309 num_examples: 165 - name: validation num_bytes: 8976.297844546049 num_examples: 18 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 196270 dataset_size: 93046.27124639563 - config_name: high_school_geography features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 98244.95755590372 num_examples: 198 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 38255 dataset_size: 111415.16369338983 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 95764.02428428999 num_examples: 193 - name: validation num_bytes: 10472.347485303722 num_examples: 21 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 52963 dataset_size: 108435.5472081902 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 193512.79518587096 num_examples: 390 - name: validation num_bytes: 21443.378184193338 num_examples: 43 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 68758 dataset_size: 217155.34880866078 - config_name: high_school_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 133970.39666714144 num_examples: 270 - name: validation num_bytes: 14461.813193990856 num_examples: 29 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 45210 dataset_size: 150631.38529972878 - config_name: high_school_microeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 118092.42372881356 num_examples: 238 - name: validation num_bytes: 12965.76355323318 num_examples: 26 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 49885 dataset_size: 133257.36272064323 - config_name: high_school_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 74924.18480273466 num_examples: 151 - name: validation num_bytes: 8477.614630960157 num_examples: 17 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 45483 dataset_size: 85600.9748722913 - config_name: high_school_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 270421.7266058966 num_examples: 545 - name: validation num_bytes: 29920.992815153495 num_examples: 60 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 113158 dataset_size: 302541.8948596466 - config_name: high_school_statistics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 107176.31733371314 num_examples: 216 - name: validation num_bytes: 11469.713912475507 num_examples: 23 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 74924 dataset_size: 120845.20668478514 - config_name: high_school_us_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 101222.0774818402 num_examples: 204 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 200043 dataset_size: 114392.2836193263 - config_name: high_school_world_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 117596.23707449081 num_examples: 237 - name: validation num_bytes: 12965.76355323318 num_examples: 26 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 250302 dataset_size: 132761.17606632048 - config_name: human_aging features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 110649.62391397236 num_examples: 223 - name: validation num_bytes: 11469.713912475507 num_examples: 23 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 41196 dataset_size: 124318.51326504436 - config_name: human_sexuality features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 65000.451716279735 num_examples: 131 - name: validation num_bytes: 5984.198563030699 num_examples: 12 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 32533 dataset_size: 73183.82571790692 - config_name: international_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 60038.58517305227 num_examples: 121 - name: validation num_bytes: 6482.88177661659 num_examples: 13 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 41592 dataset_size: 68720.64238826535 - config_name: jurisprudence features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 53588.15866685657 num_examples: 108 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 33578 dataset_size: 61272.84945489787 - config_name: logical_fallacies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 80878.4246546076 num_examples: 163 - name: validation num_bytes: 8976.297844546049 num_examples: 18 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 33669 dataset_size: 92053.89793775014 - config_name: machine_learning features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 55572.90528414756 num_examples: 112 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 31121 dataset_size: 63257.596072188855 - config_name: management features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 51107.225395242844 num_examples: 103 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 22828 dataset_size: 58791.91618328414 - config_name: marketing features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 116107.67711152257 num_examples: 234 - name: validation num_bytes: 12467.08033964729 num_examples: 25 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 49747 dataset_size: 130773.93288976635 - config_name: medical_genetics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 25775 dataset_size: 57303.3562203159 - config_name: miscellaneous features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 388514.15033471014 num_examples: 783 - name: validation num_bytes: 42886.756368386676 num_examples: 86 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 115097 dataset_size: 433600.08214169333 - config_name: moral_disputes features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 171680.58239567012 num_examples: 346 - name: validation num_bytes: 18949.96211626388 num_examples: 38 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 76043 dataset_size: 192829.71995053047 - config_name: moral_scenarios features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 444087.05561885773 num_examples: 895 - name: validation num_bytes: 49868.32135858916 num_examples: 100 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 109869 dataset_size: 496154.5524160434 - config_name: nutrition features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 151833.1162227603 num_examples: 306 - name: validation num_bytes: 16456.54604833442 num_examples: 33 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 69050 dataset_size: 170488.8377096912 - config_name: philosophy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 154314.04949437402 num_examples: 311 - name: validation num_bytes: 16955.229261920314 num_examples: 34 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 61912 dataset_size: 173468.45419489083 - config_name: prehistory features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 160764.47600056973 num_examples: 324 - name: validation num_bytes: 17453.912475506204 num_examples: 35 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 68826 dataset_size: 180417.5639146724 - config_name: professional_accounting features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 139924.6365190144 num_examples: 282 - name: validation num_bytes: 15459.179621162639 num_examples: 31 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 87297 dataset_size: 157582.99157877354 - config_name: professional_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 761150.3277310925 num_examples: 1534 - name: validation num_bytes: 84776.14630960157 num_examples: 170 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 1167828 dataset_size: 848125.6494792906 - config_name: professional_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 134962.7699757869 num_examples: 272 - name: validation num_bytes: 15459.179621162639 num_examples: 31 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 153242 dataset_size: 152621.12503554605 - config_name: professional_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 303666.2324455206 num_examples: 612 - name: validation num_bytes: 34409.14173742652 num_examples: 69 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 159357 dataset_size: 340274.5496215436 - config_name: public_relations features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 54580.53197550207 num_examples: 110 - name: validation num_bytes: 5984.198563030699 num_examples: 12 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 31500 dataset_size: 62763.90597712925 - config_name: security_studies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 121565.73030907278 num_examples: 245 - name: validation num_bytes: 13464.446766819072 num_examples: 27 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 140258 dataset_size: 137229.35251448833 - config_name: sociology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 99733.51751887196 num_examples: 201 - name: validation num_bytes: 10971.030698889615 num_examples: 22 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 56480 dataset_size: 112903.72365635807 - config_name: us_foreign_policy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 49618.6654322746 num_examples: 100 - name: validation num_bytes: 5485.515349444808 num_examples: 11 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 29027 dataset_size: 57303.3562203159 - config_name: virology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 82366.98461757584 num_examples: 166 - name: validation num_bytes: 8976.297844546049 num_examples: 18 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 38229 dataset_size: 93542.45790071838 - config_name: world_religions features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 84847.91788918957 num_examples: 171 - name: validation num_bytes: 9474.98105813194 num_examples: 19 - name: dev num_bytes: 2199.1754385964914 num_examples: 5 download_size: 27165 dataset_size: 96522.07438591801 configs: - config_name: abstract_algebra data_files: - split: test path: abstract_algebra/test-* - split: validation path: abstract_algebra/validation-* - split: dev path: abstract_algebra/dev-* - config_name: all data_files: - split: test path: all/test-* - split: validation path: all/validation-* - split: dev path: all/dev-* - split: auxiliary_train path: all/auxiliary_train-* - config_name: anatomy data_files: - split: test path: anatomy/test-* - split: validation path: anatomy/validation-* - split: dev path: anatomy/dev-* - config_name: astronomy data_files: - split: test path: astronomy/test-* - split: validation path: astronomy/validation-* - split: dev path: astronomy/dev-* - config_name: auxiliary_train data_files: - split: train path: auxiliary_train/train-* - config_name: business_ethics data_files: - split: test path: business_ethics/test-* - split: validation path: business_ethics/validation-* - split: dev path: business_ethics/dev-* - config_name: clinical_knowledge data_files: - split: test path: clinical_knowledge/test-* - split: validation path: clinical_knowledge/validation-* - split: dev path: clinical_knowledge/dev-* - config_name: college_biology data_files: - split: test path: college_biology/test-* - split: validation path: college_biology/validation-* - split: dev path: college_biology/dev-* - config_name: college_chemistry data_files: - split: test path: college_chemistry/test-* - split: validation path: college_chemistry/validation-* - split: dev path: college_chemistry/dev-* - config_name: college_computer_science data_files: - split: test path: college_computer_science/test-* - split: validation path: college_computer_science/validation-* - split: dev path: college_computer_science/dev-* - config_name: college_mathematics data_files: - split: test path: college_mathematics/test-* - split: validation path: college_mathematics/validation-* - split: dev path: college_mathematics/dev-* - config_name: college_medicine data_files: - split: test path: college_medicine/test-* - split: validation path: college_medicine/validation-* - split: dev path: college_medicine/dev-* - config_name: college_physics data_files: - split: test path: college_physics/test-* - split: validation path: college_physics/validation-* - split: dev path: college_physics/dev-* - config_name: computer_security data_files: - split: test path: computer_security/test-* - split: validation path: computer_security/validation-* - split: dev path: computer_security/dev-* - config_name: conceptual_physics data_files: - split: test path: conceptual_physics/test-* - split: validation path: conceptual_physics/validation-* - split: dev path: conceptual_physics/dev-* - config_name: econometrics data_files: - split: test path: econometrics/test-* - split: validation path: econometrics/validation-* - split: dev path: econometrics/dev-* - config_name: electrical_engineering data_files: - split: test path: electrical_engineering/test-* - split: validation path: electrical_engineering/validation-* - split: dev path: electrical_engineering/dev-* - config_name: elementary_mathematics data_files: - split: test path: elementary_mathematics/test-* - split: validation path: elementary_mathematics/validation-* - split: dev path: elementary_mathematics/dev-* - config_name: formal_logic data_files: - split: test path: formal_logic/test-* - split: validation path: formal_logic/validation-* - split: dev path: formal_logic/dev-* - config_name: global_facts data_files: - split: test path: global_facts/test-* - split: validation path: global_facts/validation-* - split: dev path: global_facts/dev-* - config_name: high_school_biology data_files: - split: test path: high_school_biology/test-* - split: validation path: high_school_biology/validation-* - split: dev path: high_school_biology/dev-* - config_name: high_school_chemistry data_files: - split: test path: high_school_chemistry/test-* - split: validation path: high_school_chemistry/validation-* - split: dev path: high_school_chemistry/dev-* - config_name: high_school_computer_science data_files: - split: test path: high_school_computer_science/test-* - split: validation path: high_school_computer_science/validation-* - split: dev path: high_school_computer_science/dev-* - config_name: high_school_european_history data_files: - split: test path: high_school_european_history/test-* - split: validation path: high_school_european_history/validation-* - split: dev path: high_school_european_history/dev-* - config_name: high_school_geography data_files: - split: test path: high_school_geography/test-* - split: validation path: high_school_geography/validation-* - split: dev path: high_school_geography/dev-* - config_name: high_school_government_and_politics data_files: - split: test path: high_school_government_and_politics/test-* - split: validation path: high_school_government_and_politics/validation-* - split: dev path: high_school_government_and_politics/dev-* - config_name: high_school_macroeconomics data_files: - split: test path: high_school_macroeconomics/test-* - split: validation path: high_school_macroeconomics/validation-* - split: dev path: high_school_macroeconomics/dev-* - config_name: high_school_mathematics data_files: - split: test path: high_school_mathematics/test-* - split: validation path: high_school_mathematics/validation-* - split: dev path: high_school_mathematics/dev-* - config_name: high_school_microeconomics data_files: - split: test path: high_school_microeconomics/test-* - split: validation path: high_school_microeconomics/validation-* - split: dev path: high_school_microeconomics/dev-* - config_name: high_school_physics data_files: - split: test path: high_school_physics/test-* - split: validation path: high_school_physics/validation-* - split: dev path: high_school_physics/dev-* - config_name: high_school_psychology data_files: - split: test path: high_school_psychology/test-* - split: validation path: high_school_psychology/validation-* - split: dev path: high_school_psychology/dev-* - config_name: high_school_statistics data_files: - split: test path: high_school_statistics/test-* - split: validation path: high_school_statistics/validation-* - split: dev path: high_school_statistics/dev-* - config_name: high_school_us_history data_files: - split: test path: high_school_us_history/test-* - split: validation path: high_school_us_history/validation-* - split: dev path: high_school_us_history/dev-* - config_name: high_school_world_history data_files: - split: test path: high_school_world_history/test-* - split: validation path: high_school_world_history/validation-* - split: dev path: high_school_world_history/dev-* - config_name: human_aging data_files: - split: test path: human_aging/test-* - split: validation path: human_aging/validation-* - split: dev path: human_aging/dev-* - config_name: human_sexuality data_files: - split: test path: human_sexuality/test-* - split: validation path: human_sexuality/validation-* - split: dev path: human_sexuality/dev-* - config_name: international_law data_files: - split: test path: international_law/test-* - split: validation path: international_law/validation-* - split: dev path: international_law/dev-* - config_name: jurisprudence data_files: - split: test path: jurisprudence/test-* - split: validation path: jurisprudence/validation-* - split: dev path: jurisprudence/dev-* - config_name: logical_fallacies data_files: - split: test path: logical_fallacies/test-* - split: validation path: logical_fallacies/validation-* - split: dev path: logical_fallacies/dev-* - config_name: machine_learning data_files: - split: test path: machine_learning/test-* - split: validation path: machine_learning/validation-* - split: dev path: machine_learning/dev-* - config_name: management data_files: - split: test path: management/test-* - split: validation path: management/validation-* - split: dev path: management/dev-* - config_name: marketing data_files: - split: test path: marketing/test-* - split: validation path: marketing/validation-* - split: dev path: marketing/dev-* - config_name: medical_genetics data_files: - split: test path: medical_genetics/test-* - split: validation path: medical_genetics/validation-* - split: dev path: medical_genetics/dev-* - config_name: miscellaneous data_files: - split: test path: miscellaneous/test-* - split: validation path: miscellaneous/validation-* - split: dev path: miscellaneous/dev-* - config_name: moral_disputes data_files: - split: test path: moral_disputes/test-* - split: validation path: moral_disputes/validation-* - split: dev path: moral_disputes/dev-* - config_name: moral_scenarios data_files: - split: test path: moral_scenarios/test-* - split: validation path: moral_scenarios/validation-* - split: dev path: moral_scenarios/dev-* - config_name: nutrition data_files: - split: test path: nutrition/test-* - split: validation path: nutrition/validation-* - split: dev path: nutrition/dev-* - config_name: philosophy data_files: - split: test path: philosophy/test-* - split: validation path: philosophy/validation-* - split: dev path: philosophy/dev-* - config_name: prehistory data_files: - split: test path: prehistory/test-* - split: validation path: prehistory/validation-* - split: dev path: prehistory/dev-* - config_name: professional_accounting data_files: - split: test path: professional_accounting/test-* - split: validation path: professional_accounting/validation-* - split: dev path: professional_accounting/dev-* - config_name: professional_law data_files: - split: test path: professional_law/test-* - split: validation path: professional_law/validation-* - split: dev path: professional_law/dev-* - config_name: professional_medicine data_files: - split: test path: professional_medicine/test-* - split: validation path: professional_medicine/validation-* - split: dev path: professional_medicine/dev-* - config_name: professional_psychology data_files: - split: test path: professional_psychology/test-* - split: validation path: professional_psychology/validation-* - split: dev path: professional_psychology/dev-* - config_name: public_relations data_files: - split: test path: public_relations/test-* - split: validation path: public_relations/validation-* - split: dev path: public_relations/dev-* - config_name: security_studies data_files: - split: test path: security_studies/test-* - split: validation path: security_studies/validation-* - split: dev path: security_studies/dev-* - config_name: sociology data_files: - split: test path: sociology/test-* - split: validation path: sociology/validation-* - split: dev path: sociology/dev-* - config_name: us_foreign_policy data_files: - split: test path: us_foreign_policy/test-* - split: validation path: us_foreign_policy/validation-* - split: dev path: us_foreign_policy/dev-* - config_name: virology data_files: - split: test path: virology/test-* - split: validation path: virology/validation-* - split: dev path: virology/dev-* - config_name: world_religions data_files: - split: test path: world_religions/test-* - split: validation path: world_religions/validation-* - split: dev path: world_religions/dev-* --- # Dataset Card for MMLU ## 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 - **Repository**: https://github.com/hendrycks/test - **Paper**: https://arxiv.org/abs/2009.03300 ### Dataset Summary [Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions'] ### Supported Tasks and Leaderboards | Model | Authors | Humanities | Social Science | STEM | Other | Average | |------------------------------------|----------|:-------:|:-------:|:-------:|:-------:|:-------:| | [UnifiedQA](https://arxiv.org/abs/2005.00700) | Khashabi et al., 2020 | 45.6 | 56.6 | 40.2 | 54.6 | 48.9 | [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) | Brown et al., 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9 | [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al., 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4 | Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 ### Languages English ## Dataset Structure ### Data Instances An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"], "answer": "D" } ``` ### Data Fields - `question`: a string feature - `choices`: a list of 4 string features - `answer`: a ClassLabel feature ### Data Splits - `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc. - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask | | auxiliary_train | dev | val | test | | ----- | :------: | :-----: | :-----: | :-----: | | TOTAL | 99842 | 285 | 1531 | 14042 ## Dataset Creation ### Curation Rationale Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn. ### 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 [MIT License](https://github.com/hendrycks/test/blob/master/LICENSE) ### Citation Information If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from: ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ``` ### Contributions Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.

lukaemon/bbh

lukaemon

"2023-02-02T01:14:46"

565,185

45

[ "size_categories:1K<n<10K", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]

null

"2023-02-01T07:46:51"

--- dataset_info: - config_name: boolean_expressions features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 11790 num_examples: 250 download_size: 17172 dataset_size: 11790 - config_name: causal_judgement features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 198021 num_examples: 187 download_size: 202943 dataset_size: 198021 - config_name: date_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 54666 num_examples: 250 download_size: 61760 dataset_size: 54666 - config_name: disambiguation_qa features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 78620 num_examples: 250 download_size: 85255 dataset_size: 78620 - config_name: dyck_languages features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38432 num_examples: 250 download_size: 43814 dataset_size: 38432 - config_name: formal_fallacies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 138224 num_examples: 250 download_size: 145562 dataset_size: 138224 - config_name: geometric_shapes features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 68560 num_examples: 250 download_size: 77242 dataset_size: 68560 - config_name: hyperbaton features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38574 num_examples: 250 download_size: 44706 dataset_size: 38574 - config_name: logical_deduction_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 148595 num_examples: 250 download_size: 155477 dataset_size: 148595 - config_name: logical_deduction_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 191022 num_examples: 250 download_size: 198404 dataset_size: 191022 - config_name: logical_deduction_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 105831 num_examples: 250 download_size: 112213 dataset_size: 105831 - config_name: movie_recommendation features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 50985 num_examples: 250 download_size: 57684 dataset_size: 50985 - config_name: multistep_arithmetic_two features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 12943 num_examples: 250 download_size: 18325 dataset_size: 12943 - config_name: navigate features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 49031 num_examples: 250 download_size: 55163 dataset_size: 49031 - config_name: object_counting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 30508 num_examples: 250 download_size: 35890 dataset_size: 30508 - config_name: penguins_in_a_table features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 70062 num_examples: 146 download_size: 74516 dataset_size: 70062 - config_name: reasoning_about_colored_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 89579 num_examples: 250 download_size: 98694 dataset_size: 89579 - config_name: ruin_names features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 46537 num_examples: 250 download_size: 53178 dataset_size: 46537 - config_name: salient_translation_error_detection features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 277110 num_examples: 250 download_size: 286443 dataset_size: 277110 - config_name: snarks features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38223 num_examples: 178 download_size: 42646 dataset_size: 38223 - config_name: sports_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 22723 num_examples: 250 download_size: 28617 dataset_size: 22723 - config_name: temporal_sequences features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 139546 num_examples: 250 download_size: 148176 dataset_size: 139546 - config_name: tracking_shuffled_objects_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 162590 num_examples: 250 download_size: 169722 dataset_size: 162590 - config_name: tracking_shuffled_objects_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 207274 num_examples: 250 download_size: 214906 dataset_size: 207274 - config_name: tracking_shuffled_objects_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 122104 num_examples: 250 download_size: 128736 dataset_size: 122104 - config_name: web_of_lies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 47582 num_examples: 250 download_size: 52964 dataset_size: 47582 - config_name: word_sorting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 60918 num_examples: 250 download_size: 66300 dataset_size: 60918 --- # BIG-bench Hard dataset homepage: https://github.com/suzgunmirac/BIG-Bench-Hard ``` @article{suzgun2022challenging, title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them}, author={Suzgun, Mirac and Scales, Nathan and Sch{\"a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V and Chi, Ed H and Zhou, Denny and and Wei, Jason}, journal={arXiv preprint arXiv:2210.09261}, year={2022} } ```

chansung/requested-arxiv-ids-3

chansung

"2024-05-15T21:10:31"

552,869

1

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

null

"2024-03-06T04:21:39"

--- dataset_info: features: - name: Requested arXiv IDs sequence: string splits: - name: train num_bytes: 7.5 num_examples: 1 download_size: 1042 dataset_size: 7.5 configs: - config_name: default data_files: - split: train path: data/train-* ---

lmms-lab/LMMs-Eval-Lite

lmms-lab

"2024-07-04T04:16:56"

552,775

2

[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

null

"2024-06-27T03:29:05"

--- dataset_info: - config_name: ai2d features: - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image dtype: image splits: - name: lite num_bytes: 90543302.1658031 num_examples: 500 download_size: 81458737 dataset_size: 90543302.1658031 - config_name: chartqa features: - name: type dtype: string - name: question dtype: string - name: answer dtype: string - name: image dtype: image splits: - name: lite num_bytes: 23170424.2 num_examples: 500 download_size: 23219432 dataset_size: 23170424.2 - config_name: coco2017_cap_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answer sequence: string - name: id dtype: int64 - name: license dtype: int8 - name: file_name dtype: string - name: coco_url dtype: string - name: height dtype: int32 - name: width dtype: int32 - name: date_captured dtype: string splits: - name: lite num_bytes: 81724646.1 num_examples: 500 download_size: 81036195 dataset_size: 81724646.1 - config_name: docvqa_val features: - name: questionId dtype: string - name: question dtype: string - name: question_types sequence: string - name: image dtype: image - name: docId dtype: int64 - name: ucsf_document_id dtype: string - name: ucsf_document_page_no dtype: string - name: answers sequence: string - name: data_split dtype: string splits: - name: lite num_bytes: 334538449.19872874 num_examples: 500 download_size: 249349131 dataset_size: 334538449.19872874 - config_name: flickr30k_test features: - name: image dtype: image - name: caption sequence: string - name: sentids sequence: string - name: img_id dtype: string - name: filename dtype: string splits: - name: lite num_bytes: 69689341.17644653 num_examples: 500 download_size: 66621555 dataset_size: 69689341.17644653 - config_name: gqa features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: lite num_bytes: 243022.3008427413 num_examples: 500 download_size: 107530 dataset_size: 243022.3008427413 - config_name: infovqa_val features: - name: questionId dtype: string - name: question dtype: string - name: answers sequence: string - name: answer_type sequence: string - name: image dtype: image - name: image_url dtype: string - name: operation/reasoning sequence: string - name: ocr dtype: string - name: data_split dtype: string splits: - name: lite num_bytes: 304765105.6765441 num_examples: 500 download_size: 233689969 dataset_size: 304765105.6765441 - config_name: mmbench_cn_dev features: - name: index dtype: int64 - name: question dtype: string - name: hint dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string - name: L2-category dtype: string - name: comment dtype: string - name: split dtype: string splits: - name: lite num_bytes: 11861120.35112035 num_examples: 500 download_size: 12795903 dataset_size: 11861120.35112035 - config_name: mmbench_en_dev features: - name: index dtype: int64 - name: question dtype: string - name: hint dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string - name: L2-category dtype: string - name: comment dtype: string - name: split dtype: string splits: - name: lite num_bytes: 11871291.175791176 num_examples: 500 download_size: 12524588 dataset_size: 11871291.175791176 - config_name: nocaps_val features: - name: image dtype: image - name: image_coco_url dtype: string - name: image_date_captured dtype: string - name: image_file_name dtype: string - name: image_height dtype: int32 - name: image_width dtype: int32 - name: image_id dtype: int32 - name: image_license dtype: int8 - name: image_open_images_id dtype: string - name: annotations_ids sequence: int32 - name: annotations_captions sequence: string splits: - name: lite num_bytes: 157984760.66666666 num_examples: 500 download_size: 155545761 dataset_size: 157984760.66666666 - config_name: ok_vqa_val2014 features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answers sequence: string - name: question_type dtype: string - name: answer_type dtype: string splits: - name: lite num_bytes: 82607924.29647246 num_examples: 500 download_size: 80223931 dataset_size: 82607924.29647246 - config_name: refcoco_bbox_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answer sequence: string - name: segmentation sequence: float32 - name: bbox sequence: float32 - name: iscrowd dtype: int8 - name: file_name dtype: string splits: - name: lite num_bytes: 87885477.24435365 num_examples: 500 download_size: 88424601 dataset_size: 87885477.24435365 - config_name: seedbench features: - name: answer dtype: string - name: choice_a dtype: string - name: choice_b dtype: string - name: choice_c dtype: string - name: choice_d dtype: string - name: data_id dtype: string - name: data_type dtype: string - name: question dtype: string - name: question_id dtype: string - name: question_type_id dtype: int16 - name: image sequence: image - name: segment sequence: int64 splits: - name: lite num_bytes: 755921749.3379655 num_examples: 500 download_size: 181839440 dataset_size: 755921749.3379655 - config_name: textcaps_val features: - name: question_id dtype: string - name: question dtype: string - name: image dtype: image - name: image_id dtype: string - name: image_classes sequence: string - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: image_width dtype: int64 - name: image_height dtype: int64 - name: set_name dtype: string - name: image_name dtype: string - name: image_path dtype: string - name: caption_id sequence: int64 - name: caption_str sequence: string - name: reference_strs sequence: string splits: - name: lite num_bytes: 145274544.53569174 num_examples: 500 download_size: 135721574 dataset_size: 145274544.53569174 - config_name: textvqa_val features: - name: image_id dtype: string - name: question_id dtype: int32 - name: question dtype: string - name: question_tokens sequence: string - name: image dtype: image - name: image_width dtype: int32 - name: image_height dtype: int32 - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: answers sequence: string - name: image_classes sequence: string - name: set_name dtype: string - name: ocr_tokens sequence: string splits: - name: lite num_bytes: 143485382.6 num_examples: 500 download_size: 139843809 dataset_size: 143485382.6 - config_name: vizwiz_vqa_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answers sequence: string - name: category dtype: string splits: - name: lite num_bytes: 242880108.01111367 num_examples: 500 download_size: 232689462 dataset_size: 242880108.01111367 - config_name: vqav2_val features: - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: image_id dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: image dtype: image splits: - name: lite num_bytes: 79046522.98300941 num_examples: 500 download_size: 78981610 dataset_size: 79046522.98300941 configs: - config_name: ai2d data_files: - split: lite path: ai2d/lite-* - config_name: chartqa data_files: - split: lite path: chartqa/lite-* - config_name: coco2017_cap_val data_files: - split: lite path: coco2017_cap_val/lite-* - config_name: docvqa_val data_files: - split: lite path: docvqa_val/lite-* - config_name: flickr30k_test data_files: - split: lite path: flickr30k_test/lite-* - config_name: gqa data_files: - split: lite path: gqa/lite-* - config_name: infovqa_val data_files: - split: lite path: infovqa_val/lite-* - config_name: mmbench_cn_dev data_files: - split: lite path: mmbench_cn_dev/lite-* - config_name: mmbench_en_dev data_files: - split: lite path: mmbench_en_dev/lite-* - config_name: nocaps_val data_files: - split: lite path: nocaps_val/lite-* - config_name: ok_vqa_val2014 data_files: - split: lite path: ok_vqa_val2014/lite-* - config_name: refcoco_bbox_val data_files: - split: lite path: refcoco_bbox_val/lite-* - config_name: seedbench data_files: - split: lite path: seedbench/lite-* - config_name: textcaps_val data_files: - split: lite path: textcaps_val/lite-* - config_name: textvqa_val data_files: - split: lite path: textvqa_val/lite-* - config_name: vizwiz_vqa_val data_files: - split: lite path: vizwiz_vqa_val/lite-* - config_name: vqav2_val data_files: - split: lite path: vqav2_val/lite-* ---

trl-internal-testing/zen

trl-internal-testing

"2024-09-13T21:03:59"

544,189

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

null

"2024-09-13T21:03:47"

--- dataset_info: - config_name: conversational_implicit_prompt_preference features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2810.3684210526317 num_examples: 17 - name: test num_bytes: 253 num_examples: 2 download_size: 7230 dataset_size: 3063.3684210526317 - config_name: conversational_language_modeling features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1439.6315789473683 num_examples: 17 - name: test num_bytes: 169.3684210526316 num_examples: 2 download_size: 3950 dataset_size: 1609.0 - config_name: conversational_preference features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2116.0526315789475 num_examples: 17 - name: test num_bytes: 248.94736842105263 num_examples: 2 download_size: 9108 dataset_size: 2365.0 - config_name: conversational_prompt_completion features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1507.6315789473683 num_examples: 17 - name: test num_bytes: 177.3684210526316 num_examples: 2 download_size: 6364 dataset_size: 1685.0 - config_name: conversational_prompt_only features: - name: prompt list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 830.3157894736842 num_examples: 17 - name: test num_bytes: 97.6842105263158 num_examples: 2 download_size: 3676 dataset_size: 928.0 - config_name: conversational_unpaired_preference features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string - name: label dtype: bool splits: - name: train num_bytes: 1484.3684210526317 num_examples: 17 - name: test num_bytes: 174.6315789473684 num_examples: 2 download_size: 7196 dataset_size: 1659.0 - config_name: standard_implicit_prompt_preference features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 1606.0526315789473 num_examples: 17 - name: test num_bytes: 188.94736842105263 num_examples: 2 download_size: 4688 dataset_size: 1795.0 - config_name: standard_language_modeling features: - name: text dtype: string splits: - name: train num_bytes: 787.3684210526316 num_examples: 17 - name: test num_bytes: 92.63157894736842 num_examples: 2 download_size: 2626 dataset_size: 880.0 - config_name: standard_preference features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 1268.7368421052631 num_examples: 17 - name: test num_bytes: 149.26315789473685 num_examples: 2 download_size: 4953 dataset_size: 1418.0 - config_name: standard_prompt_completion features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 855.3684210526316 num_examples: 17 - name: test num_bytes: 100.63157894736842 num_examples: 2 download_size: 3473 dataset_size: 956.0 - config_name: standard_prompt_only features: - name: prompt dtype: string splits: - name: train num_bytes: 473.3157894736842 num_examples: 17 - name: test num_bytes: 55.68421052631579 num_examples: 2 download_size: 2160 dataset_size: 529.0 - config_name: standard_unpaired_preference features: - name: prompt dtype: string - name: completion dtype: string - name: label dtype: bool splits: - name: train num_bytes: 867.8947368421053 num_examples: 17 - name: test num_bytes: 102.10526315789474 num_examples: 2 download_size: 4364 dataset_size: 970.0 configs: - config_name: conversational_implicit_prompt_preference data_files: - split: train path: conversational_implicit_prompt_preference/train-* - split: test path: conversational_implicit_prompt_preference/test-* - config_name: conversational_language_modeling data_files: - split: train path: conversational_language_modeling/train-* - split: test path: conversational_language_modeling/test-* - config_name: conversational_preference data_files: - split: train path: conversational_preference/train-* - split: test path: conversational_preference/test-* - config_name: conversational_prompt_completion data_files: - split: train path: conversational_prompt_completion/train-* - split: test path: conversational_prompt_completion/test-* - config_name: conversational_prompt_only data_files: - split: train path: conversational_prompt_only/train-* - split: test path: conversational_prompt_only/test-* - config_name: conversational_unpaired_preference data_files: - split: train path: conversational_unpaired_preference/train-* - split: test path: conversational_unpaired_preference/test-* - config_name: standard_implicit_prompt_preference data_files: - split: train path: standard_implicit_prompt_preference/train-* - split: test path: standard_implicit_prompt_preference/test-* - config_name: standard_language_modeling data_files: - split: train path: standard_language_modeling/train-* - split: test path: standard_language_modeling/test-* - config_name: standard_preference data_files: - split: train path: standard_preference/train-* - split: test path: standard_preference/test-* - config_name: standard_prompt_completion data_files: - split: train path: standard_prompt_completion/train-* - split: test path: standard_prompt_completion/test-* - config_name: standard_prompt_only data_files: - split: train path: standard_prompt_only/train-* - split: test path: standard_prompt_only/test-* - config_name: standard_unpaired_preference data_files: - split: train path: standard_unpaired_preference/train-* - split: test path: standard_unpaired_preference/test-* ---

Hennara/ammlu

Hennara

"2024-03-02T17:20:25"

543,776

0

[ "task_categories:question-answering", "language:ar", "size_categories:10K<n<100K", "arxiv:2009.03300", "arxiv:2309.12053", "region:us" ]

[ "question-answering" ]

"2024-02-06T06:11:42"

--- task_categories: - question-answering language: - ar size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name Arabic MMLU: Measuring massive multitask language understanding in Arabic This dataset has been translated from the original MMLU with the help of GPT-4. The original data paper [MMLU](https://arxiv.org/pdf/2009.03300v3.pdf) The MMLU dataset on huggingface [MMLU](cais/mmlu) ### Dataset Sources [optional] The translation and re-generation has been done by AceGPT researchers [AceGPT](https://arxiv.org/abs/2309.12053) - [**Repository:**](https://github.com/FreedomIntelligence/AceGPT/tree/main/eval/benchmark_eval/benchmarks/MMLUArabic) - [**Paper**](https://arxiv.org/abs/2309.12053) ## Uses Arabic-MMLU is a comprehensive evaluation benchmark specifically designed to evaluate the knowledge and reasoning abilities of LLMs within the context of Arabic language and culture. Arabic-MMLU covers a wide range of subjects, comprising 57 topics that span from elementary to advanced professional levels. ### Direct Use This dataset is available to used directly using [datasets](https://github.com/huggingface/datasets) from huggingface, also is availabe to use with [lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) framework. ## Dataset Structure The dataset consist of 57 subject, divided into 4 category. | Subject Area | STEM | Humanities | Social Sciences | Other | |---|---|---|---|---| | abstract_algebra | ✓ | | | | | anatomy | ✓ | | | | | astronomy | ✓ | | | | | business_ethics | | | | ✓ | | clinical_knowledge | | | | ✓ | | college_biology | ✓ | | | | | college_chemistry | ✓ | | | | | college_computer_science | ✓ | | | | | college_mathematics | ✓ | | | | | college_medicine | | | | ✓ | | college_physics | ✓ | | | | | computer_security | ✓ | | | | | conceptual_physics | ✓ | | | | | econometrics | | | ✓ | | | electrical_engineering | ✓ | | | | | elementary_mathematics | ✓ | | | | | formal_logic | | ✓ | | | | global_facts | | | | ✓ | | high_school_biology | ✓ | | | | | high_school_chemistry | ✓ | | | | | high_school_computer_science | ✓ | | | | | high_school_european_history | | ✓ | | | | high_school_geography | | | ✓ | | | high_school_government_and_politics | | | ✓ | | | high_school_macroeconomics | | | ✓ | | | high_school_mathematics | ✓ | | | | | high_school_microeconomics | | | ✓ | | | high_school_physics | ✓ | | | | | high_school_psychology | | | ✓ | | | high_school_statistics | ✓ | | | | | high_school_us_history | | ✓ | | | | high_school_world_history | | ✓ | | | | human_aging | | | | ✓ | | human_sexuality | | | ✓ | | | international_law | | ✓ | | | | jurisprudence | | ✓ | | | | logical_fallacies | | ✓ | | | | machine_learning | ✓ | | | | | management | | | | ✓ | | marketing | | | | ✓ | | medical_genetics | | | | ✓ | | miscellaneous | | | | ✓ | | moral_disputes | | ✓ | | | | moral_scenarios | | ✓ | | | | nutrition | | | | ✓ | | philosophy | | ✓ | | | | prehistory | | ✓ | | | | professional_accounting | | | | ✓ | | professional_law | | ✓ | | | | professional_medicine | | | | ✓ | | professional_psychology | | | ✓ | | | public_relations | | | ✓ | | | security_studies | | | ✓ | | | sociology | | | ✓ | | | us_foreign_policy | | | ✓ | | | virology | | | | ✓ | | world_religions | | ✓ | | | | - | - | - | - | - | each item of the dataset is a dictionary with **Question, A, B, C, D, Answer** where A,B,C,D are options to the choose from. here is three example from the abstract algebra subject. | Question | A | B | C | D | Answer | |---|---|---|---|---|---| | مجموعة فرعية H من مجموعة (G،*) هي مجموعة إذا | 'a، b في H => a * b في H' | 'a في H => a^-1 في H' | 'a، b في H => a * b^-1 في H' | 'H يحتوي على العنصر المحدد' | C | | 'ما هو ترتيب العنصر (4، 2) من Z_12 x Z_8' | 2 | 4 | 8 | 12 | C | |ما هو الدرجة لتمديد الحقل المعطى Q(sqrt(2) + sqrt(3)) على Q| 0 | 4 | 2 | 6| B | The size of each subject within the dataset | Subject | Test Length | Eval Length | |---|---|---| | professional_law | 1534 | 5 | | moral_scenarios | 895 | 5 | | miscellaneous | 783 | 5 | | professional_psychology | 612 | 5 | | high_school_psychology | 545 | 5 | | high_school_macroeconomics | 390 | 5 | | elementary_mathematics | 378 | 5 | | moral_disputes | 346 | 5 | | prehistory | 324 | 5 | | philosophy | 311 | 5 | | high_school_biology | 310 | 5 | | nutrition | 306 | 5 | | professional_accounting | 282 | 5 | | professional_medicine | 272 | 5 | | high_school_mathematics | 270 | 5 | | clinical_knowledge | 265 | 5 | | security_studies | 245 | 5 | | high_school_microeconomics | 238 | 5 | | high_school_world_history | 237 | 5 | | conceptual_physics | 235 | 5 | | marketing | 234 | 5 | | human_aging | 223 | 5 | | high_school_statistics | 216 | 5 | | high_school_us_history | 204 | 5 | | high_school_chemistry | 203 | 5 | | sociology | 201 | 5 | | high_school_geography | 198 | 5 | | high_school_government_and_politics | 193 | 5 | | college_medicine | 173 | 5 | | world_religions | 171 | 5 | | virology | 166 | 5 | | high_school_european_history | 165 | 5 | | logical_fallacies | 163 | 5 | | astronomy | 152 | 5 | | high_school_physics | 151 | 5 | | electrical_engineering | 145 | 5 | | college_biology | 144 | 5 | | anatomy | 135 | 5 | | human_sexuality | 131 | 5 | | formal_logic | 126 | 5 | | international_law | 121 | 5 | | econometrics | 114 | 5 | | machine_learning | 112 | 5 | | public_relations | 110 | 5 | | jurisprudence | 108 | 5 | | management | 103 | 5 | | college_physics | 102 | 5 | | abstract_algebra | 100 | 5 | | business_ethics | 100 | 5 | | college_chemistry | 100 | 5 | | college_computer_science | 100 | 5 | | college_mathematics | 100 | 5 | | computer_security | 100 | 5 | | global_facts | 100 | 5 | | high_school_computer_science | 100 | 5 | | medical_genetics | 100 | 5 | | us_foreign_policy | 100 | 5 | | count | 14042 | 285 |

nlp-waseda/JMMLU

nlp-waseda

"2024-02-27T05:22:30"

514,934

7

[ "task_categories:multiple-choice", "task_categories:question-answering", "language:ja", "license:cc-by-nc-nd-4.0", "size_categories:1K<n<10K", "arxiv:2009.03300", "region:us", "llm", "evaluation", "Japanese" ]

[ "multiple-choice", "question-answering" ]

"2024-02-09T12:19:13"

--- license: cc-by-nc-nd-4.0 task_categories: - multiple-choice - question-answering language: - ja tags: - llm - evaluation - Japanese pretty_name: JMMLU size_categories: - 1K<n<10K --- # JMMLU Japanese Massive Multitask Language Understanding Benchmark JMMLU is a four-choice question set consisting of Japanese-translated questions of a portion of MMLU ([Paper](https://arxiv.org/abs/2009.03300), [Github](https://github.com/hendrycks/test)) (Translated questions) and questions based on unique Japanese cultural context (Japanese questions). It is designed to assess the performance of large language models in Japanese. For the translated questions, a maximum of 150 questions from each of the 57 MMLU tasks (subjects) were selected and first machine-translated into Japanese. Next, the translators checked the machine translations and removed questions and tasks that were difficult to translate, irrelevant, or inconsistent with the Japanese culture. The remaining questions were modified to make them fluent. The Japanese questions are based on school subjects, such as Japanese civics and history, and are manually created by Japanese teachers. The format is the same as MMLU: ``` Question, Choice A, Choice B, Choice C, Choice D, Answer ``` [Github](https://github.com/nlp-waseda/JMMLU) The JMMLU consists of 7,536 questions in the following 56 tasks (subjects). | Japanese Task Name | English Task Name | Number | |---|---|---:| | 専門医学 | professional_medicine | 150 | | 専門心理学 | professional_psychology | 150 | | 専門会計 | professional_accounting | 150 | | 哲学 | philosophy | 150 | | 雑学 | miscellaneous | 150 | | 医学遺伝学 | medical_genetics | 99 | | 形式論理 | formal_logic | 125 | | 先史学 | prehistory | 150 | | 天文学 | astronomy | 148 | | 熟語 | japanese_idiom | 150 | | 世界宗教 | world_religions | 147 | | 世界事実 | global_facts | 97 | | 世界史 | world_history | 150 | | 社会学 | sociology | 150 | | 栄養学 | nutrition | 149 | | 日本史 | japanese_history | 150 | | 日本地理 | japanese_geography | 139 | | 人間の老化 | human_aging | 150 | | 論理学 | logical_fallacies | 150 | | 倫理的議論 | moral_disputes | 148 | | 臨床知識 | clinical_knowledge | 150 | | 経営学 | management | 102 | | 解剖学 | anatomy | 132 | | 計量経済学 | econometrics | 113 | | 機械学習 | machine_learning | 111 | | 国際法 | international_law | 120 | | 公民 | japanese_civics | 150 | | 公共関係 | public_relations | 109 | | 高校心理学 | high_school_psychology | 150 | | 高校物理 | high_school_physics | 150 | | 高校統計学 | high_school_statistics | 150 | | 高校数学 | high_school_mathematics | 150 | | 高校生物学 | high_school_biology | 148 | | 高校情報科学 | high_school_computer_science | 98 | | 高校化学 | high_school_chemistry | 149 | | 高校地理 | high_school_geography | 150 | | 高校ヨーロッパ史 | high_school_european_history | 150 | | 高校ミクロ経済学 | high_school_microeconomics | 149 | | 高校マクロ経済学 | high_school_macroeconomics | 148 | | 概念物理学 | conceptual_physics | 150 | | 法理学 | jurisprudence | 107 | | 電気工学 | electrical_engineering | 144 | | 大学医学 | college_medicine | 150 | | 大学物理 | college_physics | 100 | | 大学数学 | college_mathematics | 99 | | 大学生物学 | college_biology | 143 | | 大学化学 | college_chemistry | 99 | | 大学コンピュータ科学 | college_computer_science | 99 | | 初等数学 | elementary_mathematics | 150 | | 抽象代数 | abstract_algebra | 99 | | マーケティング | marketing | 150 | | ビジネス倫理 | business_ethics | 86 | | セクシュアリティ | human_sexuality | 130 | | セキュリティ研究 | security_studies | 150 | | コンピュータセキュリティ | computer_security | 99 | | ウイルス学 | virology | 150 | The copyrights for Japanese and World History belongs to STEP Corporation. Commercial use other than for research and evaluation of language models is prohibited. The copyrights for Japanese idioms, Japansese civics, and Japanese geography belong to New Style Cram School VIST. Commercial use is allowed only for research and evaluation of language models. This work is licensed under CC BY-NC-ND 4.0 # Acknowledgment We express our gratitude to the RIKEN for their support in the translation of MMLU. We also acknowledge the contributions from Step Corporation, who provided materials on Japanese and World History, and from New Style Cram School VIST, who supplied resources on japanese_idioms, japansese_civics, and japanese_geography.

lmms-lab/MMMU

lmms-lab

"2024-03-08T05:09:42"

483,639

4

[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

null

"2024-01-15T06:32:16"

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 57719107.0 num_examples: 150 - name: validation num_bytes: 347519954.0 num_examples: 900 - name: test num_bytes: 3271046267.0 num_examples: 10500 download_size: 3377778136 dataset_size: 3676285328.0 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: validation path: data/validation-* - split: test path: data/test-* --- This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated. <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [MMMU](https://github.com/MMMU-Benchmark/MMMU). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{yue2023mmmu, title={Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi}, author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Stevens, Samuel and Jiang, Dongfu and Ren, Weiming and Sun, Yuxuan and others}, journal={arXiv preprint arXiv:2311.16502}, year={2023} } ```

allenai/ai2_arc

allenai

"2023-12-21T15:09:48"

449,088

134

[ "task_categories:question-answering", "task_ids:open-domain-qa", "task_ids:multiple-choice-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1803.05457", "region:us" ]

[ "question-answering" ]

"2022-03-02T23:29:22"

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa - multiple-choice-qa pretty_name: Ai2Arc language_bcp47: - en-US dataset_info: - config_name: ARC-Challenge features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 349760 num_examples: 1119 - name: test num_bytes: 375511 num_examples: 1172 - name: validation num_bytes: 96660 num_examples: 299 download_size: 449460 dataset_size: 821931 - config_name: ARC-Easy features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 619000 num_examples: 2251 - name: test num_bytes: 657514 num_examples: 2376 - name: validation num_bytes: 157394 num_examples: 570 download_size: 762935 dataset_size: 1433908 configs: - config_name: ARC-Challenge data_files: - split: train path: ARC-Challenge/train-* - split: test path: ARC-Challenge/test-* - split: validation path: ARC-Challenge/validation-* - config_name: ARC-Easy data_files: - split: train path: ARC-Easy/train-* - split: test path: ARC-Easy/test-* - split: validation path: ARC-Easy/validation-* --- # Dataset Card for "ai2_arc" ## 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://allenai.org/data/arc](https://allenai.org/data/arc) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1361.68 MB - **Size of the generated dataset:** 2.28 MB - **Total amount of disk used:** 1363.96 MB ### Dataset Summary A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### ARC-Challenge - **Size of downloaded dataset files:** 680.84 MB - **Size of the generated dataset:** 0.83 MB - **Total amount of disk used:** 681.67 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` #### ARC-Easy - **Size of downloaded dataset files:** 680.84 MB - **Size of the generated dataset:** 1.45 MB - **Total amount of disk used:** 682.29 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` ### Data Fields The data fields are the same among all splits. #### ARC-Challenge - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. #### ARC-Easy - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. ### Data Splits | name |train|validation|test| |-------------|----:|---------:|---:| |ARC-Challenge| 1119| 299|1172| |ARC-Easy | 2251| 570|2376| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{allenai:arc, author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, journal = {arXiv:1803.05457v1}, year = {2018}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

Datasets:

librarian-bots
/

dataset_cards_with_metadata

Dataset Card for Hugging Face Hub Dataset Cards

Dataset Details

Uses

Out-of-Scope Use

Dataset Structure

Dataset Creation

Curation Rationale

Source Data

Data Collection and Processing

Who are the source data producers?

Annotations [optional]

Annotation process

Who are the annotators?

Personal and Sensitive Information

Bias, Risks, and Limitations

Recommendations

Citation

Dataset Card Authors

Dataset Card Contact

Collection including librarian-bots/dataset_cards_with_metadata

Hub Card Data