davanstrien/popular-cards · Datasets at Hugging Face

datasetId stringlengths 2 81	card stringlengths 20 977k
hails/mmlu_no_train	--- 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.
argilla/databricks-dolly-15k-curated-en	--- 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') ```
lighteval/mmlu	--- 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.
cais/mmlu	--- 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.
lavita/medical-qa-shared-task-v1-toy	--- 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)
wikitext	--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 - gfdl multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: wikitext-2 pretty_name: WikiText dataset_info: - config_name: wikitext-103-raw-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1305088 num_examples: 4358 - name: train num_bytes: 546500949 num_examples: 1801350 - name: validation num_bytes: 1159288 num_examples: 3760 download_size: 315466397 dataset_size: 548965325 - config_name: wikitext-103-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1295575 num_examples: 4358 - name: train num_bytes: 545141915 num_examples: 1801350 - name: validation num_bytes: 1154751 num_examples: 3760 download_size: 313093838 dataset_size: 547592241 - config_name: wikitext-2-raw-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1305088 num_examples: 4358 - name: train num_bytes: 11061717 num_examples: 36718 - name: validation num_bytes: 1159288 num_examples: 3760 download_size: 7747362 dataset_size: 13526093 - config_name: wikitext-2-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1270947 num_examples: 4358 - name: train num_bytes: 10918118 num_examples: 36718 - name: validation num_bytes: 1134123 num_examples: 3760 download_size: 7371282 dataset_size: 13323188 configs: - config_name: wikitext-103-raw-v1 data_files: - split: test path: wikitext-103-raw-v1/test-* - split: train path: wikitext-103-raw-v1/train-* - split: validation path: wikitext-103-raw-v1/validation-* - config_name: wikitext-103-v1 data_files: - split: test path: wikitext-103-v1/test-* - split: train path: wikitext-103-v1/train-* - split: validation path: wikitext-103-v1/validation-* - config_name: wikitext-2-raw-v1 data_files: - split: test path: wikitext-2-raw-v1/test-* - split: train path: wikitext-2-raw-v1/train-* - split: validation path: wikitext-2-raw-v1/validation-* - config_name: wikitext-2-v1 data_files: - split: test path: wikitext-2-v1/test-* - split: train path: wikitext-2-v1/train-* - split: validation path: wikitext-2-v1/validation-* --- # Dataset Card for "wikitext" ## 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://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843) - Point of Contact: [Stephen Merity](mailto:smerity@salesforce.com) - Size of downloaded dataset files: 391.41 MB - Size of the generated dataset: 1.12 GB - Total amount of disk used: 1.52 GB ### Dataset Summary The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger vocabulary and retains the original case, punctuation and numbers - all of which are removed in PTB. As it is composed of full articles, the dataset is well suited for models that can take advantage of long term dependencies. Each subset comes in two different variants: - Raw (for character level work) contain the raw tokens, before the addition of the <unk> (unknown) tokens. - Non-raw (for word level work) contain only the tokens in their vocabulary (wiki.train.tokens, wiki.valid.tokens, and wiki.test.tokens). The out-of-vocabulary tokens have been replaced with the the <unk> token. ### 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 #### wikitext-103-raw-v1 - Size of downloaded dataset files: 191.98 MB - Size of the generated dataset: 549.42 MB - Total amount of disk used: 741.41 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "text": "\" The gold dollar or gold one @-@ dollar piece was a coin struck as a regular issue by the United States Bureau of the Mint from..." } ``` #### wikitext-103-v1 - Size of downloaded dataset files: 190.23 MB - Size of the generated dataset: 548.05 MB - Total amount of disk used: 738.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..." } ``` #### wikitext-2-raw-v1 - Size of downloaded dataset files: 4.72 MB - Size of the generated dataset: 13.54 MB - Total amount of disk used: 18.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" The Sinclair Scientific Programmable was introduced in 1975 , with the same case as the Sinclair Oxford . It was larger than t..." } ``` #### wikitext-2-v1 - Size of downloaded dataset files: 4.48 MB - Size of the generated dataset: 13.34 MB - Total amount of disk used: 17.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..." } ``` ### Data Fields The data fields are the same among all splits. #### wikitext-103-raw-v1 - `text`: a `string` feature. #### wikitext-103-v1 - `text`: a `string` feature. #### wikitext-2-raw-v1 - `text`: a `string` feature. #### wikitext-2-v1 - `text`: a `string` feature. ### Data Splits \| name \| train \|validation\|test\| \|-------------------\|------:\|---------:\|---:\| \|wikitext-103-raw-v1\|1801350\| 3760\|4358\| \|wikitext-103-v1 \|1801350\| 3760\|4358\| \|wikitext-2-raw-v1 \| 36718\| 3760\|4358\| \|wikitext-2-v1 \| 36718\| 3760\|4358\| ## 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 The dataset is available under the [Creative Commons Attribution-ShareAlike License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @misc{merity2016pointer, title={Pointer Sentinel Mixture Models}, author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher}, year={2016}, eprint={1609.07843}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
yelp_review_full	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: YelpReviewFull license_details: yelp-licence dataset_info: config_name: yelp_review_full features: - name: label dtype: class_label: names: '0': 1 star '1': 2 star '2': 3 stars '3': 4 stars '4': 5 stars - name: text dtype: string splits: - name: train num_bytes: 483811554 num_examples: 650000 - name: test num_bytes: 37271188 num_examples: 50000 download_size: 322952369 dataset_size: 521082742 configs: - config_name: yelp_review_full data_files: - split: train path: yelp_review_full/train-* - split: test path: yelp_review_full/test-* default: true train-eval-index: - config: yelp_review_full task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- --- # Dataset Card for YelpReviewFull ## 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: [Yelp](https://www.yelp.com/dataset) - Repository: [Crepe](https://github.com/zhangxiangxiao/Crepe) - Paper: [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626) - Point of Contact: [Xiang Zhang](mailto:xiang.zhang@nyu.edu) ### Dataset Summary The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment. ### Languages The reviews were mainly written in english. ## Dataset Structure ### Data Instances A typical data point, comprises of a text and the corresponding label. An example from the YelpReviewFull test set looks as follows: ``` { 'label': 0, 'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!' } ``` ### Data Fields - 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'label': Corresponds to the score associated with the review (between 1 and 5). ### Data Splits The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5. In total there are 650,000 trainig samples and 50,000 testing samples. ## Dataset Creation ### Curation Rationale The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### 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 You can check the official [yelp-dataset-agreement](https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf). ### Citation Information Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.
etechgrid/Prompts_for_Voice_cloning_and_TTS	--- license: mit ---
fsicoli/common_voice_17_0	--- license: cc0-1.0 language: - ab - af - am - ar - as - ast - az - ba - bas - be - bg - bn - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - dyu - el - en - eo - es - et - eu - fa - fi - fr - gl - gn - ha - he - hi - hsb - hu - ia - id - ig - is - it - ja - ka - kab - kk - kmr - ko - ky - lg - lo - lt - lv - mdf - mhr - mk - ml - mn - mr - mrj - mt - myv - nl - oc - or - pl - ps - pt - quy - ro - ru - rw - sah - sat - sc - sk - skr - sl - sq - sr - sw - ta - th - ti - tig - tk - tok - tr - tt - tw - ug - uk - ur - uz - vi - vot - yue - za - zgh - zh - yo task_categories: - automatic-speech-recognition pretty_name: Common Voice Corpus 17.0 size_categories: - 100B<n<1T tags: - mozilla - foundation --- # Dataset Card for Common Voice Corpus 17.0 <!-- Provide a quick summary of the dataset. --> This dataset is an unofficial version of the Mozilla Common Voice Corpus 17. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/. ## Languages ``` Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba ``` ## How to use The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function. For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese): ``` from datasets import load_dataset cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ``` from datasets import load_dataset cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train", streaming=True) print(next(iter(cv_17))) ``` Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed). ### Local ``` from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train") batch_sampler = BatchSampler(RandomSampler(cv_17), batch_size=32, drop_last=False) dataloader = DataLoader(cv_17, batch_sampler=batch_sampler) ``` ### Streaming ``` from datasets import load_dataset from torch.utils.data import DataLoader cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train") dataloader = DataLoader(cv_17, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets. ### Dataset Structure Data Instances A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment. ### Licensing Information Public Domain, CC-0 ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ---
cifar10	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-10 pretty_name: Cifar10 dataset_info: config_name: plain_text features: - name: img dtype: image - name: label dtype: class_label: names: '0': airplane '1': automobile '2': bird '3': cat '4': deer '5': dog '6': frog '7': horse '8': ship '9': truck splits: - name: train num_bytes: 113648310.0 num_examples: 50000 - name: test num_bytes: 22731580.0 num_examples: 10000 download_size: 143646105 dataset_size: 136379890.0 configs: - config_name: plain_text data_files: - split: train path: plain_text/train-* - split: test path: plain_text/test-* default: true --- # Dataset Card for CIFAR-10 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.cs.toronto.edu/~kriz/cifar.html - Repository: - Paper: Learning Multiple Layers of Features from Tiny Images by Alex Krizhevsky - Leaderboard: - Point of Contact: ### Dataset Summary The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-10). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x201FA6EE748>, 'label': 0 } ``` ### Data Fields - img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - label: 0-9 with the following correspondence 0 airplane 1 automobile 2 bird 3 cat 4 deer 5 dog 6 frog 7 horse 8 ship 9 truck ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.
cifar100	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-100 pretty_name: Cifar100 dataset_info: config_name: cifar100 features: - name: img dtype: image - name: fine_label dtype: class_label: names: '0': apple '1': aquarium_fish '2': baby '3': bear '4': beaver '5': bed '6': bee '7': beetle '8': bicycle '9': bottle '10': bowl '11': boy '12': bridge '13': bus '14': butterfly '15': camel '16': can '17': castle '18': caterpillar '19': cattle '20': chair '21': chimpanzee '22': clock '23': cloud '24': cockroach '25': couch '26': cra '27': crocodile '28': cup '29': dinosaur '30': dolphin '31': elephant '32': flatfish '33': forest '34': fox '35': girl '36': hamster '37': house '38': kangaroo '39': keyboard '40': lamp '41': lawn_mower '42': leopard '43': lion '44': lizard '45': lobster '46': man '47': maple_tree '48': motorcycle '49': mountain '50': mouse '51': mushroom '52': oak_tree '53': orange '54': orchid '55': otter '56': palm_tree '57': pear '58': pickup_truck '59': pine_tree '60': plain '61': plate '62': poppy '63': porcupine '64': possum '65': rabbit '66': raccoon '67': ray '68': road '69': rocket '70': rose '71': sea '72': seal '73': shark '74': shrew '75': skunk '76': skyscraper '77': snail '78': snake '79': spider '80': squirrel '81': streetcar '82': sunflower '83': sweet_pepper '84': table '85': tank '86': telephone '87': television '88': tiger '89': tractor '90': train '91': trout '92': tulip '93': turtle '94': wardrobe '95': whale '96': willow_tree '97': wolf '98': woman '99': worm - name: coarse_label dtype: class_label: names: '0': aquatic_mammals '1': fish '2': flowers '3': food_containers '4': fruit_and_vegetables '5': household_electrical_devices '6': household_furniture '7': insects '8': large_carnivores '9': large_man-made_outdoor_things '10': large_natural_outdoor_scenes '11': large_omnivores_and_herbivores '12': medium_mammals '13': non-insect_invertebrates '14': people '15': reptiles '16': small_mammals '17': trees '18': vehicles_1 '19': vehicles_2 splits: - name: train num_bytes: 112545106.0 num_examples: 50000 - name: test num_bytes: 22564261.0 num_examples: 10000 download_size: 142291368 dataset_size: 135109367.0 configs: - config_name: cifar100 data_files: - split: train path: cifar100/train-* - split: test path: cifar100/test-* default: true --- # Dataset Card for CIFAR-100 ## 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: [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html) - Repository: - Paper: [Paper](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass). ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-100). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19, 'coarse_label': 11 } ``` ### Data Fields - `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `fine_label`: an `int` classification label with the following mapping: `0`: apple `1`: aquarium_fish `2`: baby `3`: bear `4`: beaver `5`: bed `6`: bee `7`: beetle `8`: bicycle `9`: bottle `10`: bowl `11`: boy `12`: bridge `13`: bus `14`: butterfly `15`: camel `16`: can `17`: castle `18`: caterpillar `19`: cattle `20`: chair `21`: chimpanzee `22`: clock `23`: cloud `24`: cockroach `25`: couch `26`: cra `27`: crocodile `28`: cup `29`: dinosaur `30`: dolphin `31`: elephant `32`: flatfish `33`: forest `34`: fox `35`: girl `36`: hamster `37`: house `38`: kangaroo `39`: keyboard `40`: lamp `41`: lawn_mower `42`: leopard `43`: lion `44`: lizard `45`: lobster `46`: man `47`: maple_tree `48`: motorcycle `49`: mountain `50`: mouse `51`: mushroom `52`: oak_tree `53`: orange `54`: orchid `55`: otter `56`: palm_tree `57`: pear `58`: pickup_truck `59`: pine_tree `60`: plain `61`: plate `62`: poppy `63`: porcupine `64`: possum `65`: rabbit `66`: raccoon `67`: ray `68`: road `69`: rocket `70`: rose `71`: sea `72`: seal `73`: shark `74`: shrew `75`: skunk `76`: skyscraper `77`: snail `78`: snake `79`: spider `80`: squirrel `81`: streetcar `82`: sunflower `83`: sweet_pepper `84`: table `85`: tank `86`: telephone `87`: television `88`: tiger `89`: tractor `90`: train `91`: trout `92`: tulip `93`: turtle `94`: wardrobe `95`: whale `96`: willow_tree `97`: wolf `98`: woman `99`: worm - `coarse_label`: an `int` coarse classification label with following mapping: `0`: aquatic_mammals `1`: fish `2`: flowers `3`: food_containers `4`: fruit_and_vegetables `5`: household_electrical_devices `6`: household_furniture `7`: insects `8`: large_carnivores `9`: large_man-made_outdoor_things `10`: large_natural_outdoor_scenes `11`: large_omnivores_and_herbivores `12`: medium_mammals `13`: non-insect_invertebrates `14`: people `15`: reptiles `16`: small_mammals `17`: trees `18`: vehicles_1 `19`: vehicles_2 ### Data Splits \| name \|train\|test\| \|----------\|----:\|---------:\| \|cifar100\|50000\| 10000\| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.
winogrande	--- language: - en paperswithcode_id: winogrande pretty_name: WinoGrande dataset_info: - config_name: winogrande_xs features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 20704 num_examples: 160 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 412552 - config_name: winogrande_s features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 82308 num_examples: 640 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 474156 - config_name: winogrande_m features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 329001 num_examples: 2558 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 720849 - config_name: winogrande_l features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1319576 num_examples: 10234 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 1711424 - config_name: winogrande_xl features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 5185832 num_examples: 40398 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 5577680 - config_name: winogrande_debiased features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1203420 num_examples: 9248 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 1595268 --- # Dataset Card for "winogrande" ## 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://leaderboard.allenai.org/winogrande/submissions/get-started](https://leaderboard.allenai.org/winogrande/submissions/get-started) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 20.37 MB - Size of the generated dataset: 10.50 MB - Total amount of disk used: 30.87 MB ### Dataset Summary WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires commonsense reasoning. ### 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 #### winogrande_debiased - Size of downloaded dataset files: 3.40 MB - Size of the generated dataset: 1.59 MB - Total amount of disk used: 4.99 MB An example of 'train' looks as follows. ``` ``` #### winogrande_l - Size of downloaded dataset files: 3.40 MB - Size of the generated dataset: 1.71 MB - Total amount of disk used: 5.11 MB An example of 'validation' looks as follows. ``` ``` #### winogrande_m - Size of downloaded dataset files: 3.40 MB - Size of the generated dataset: 0.72 MB - Total amount of disk used: 4.12 MB An example of 'validation' looks as follows. ``` ``` #### winogrande_s - Size of downloaded dataset files: 3.40 MB - Size of the generated dataset: 0.47 MB - Total amount of disk used: 3.87 MB An example of 'validation' looks as follows. ``` ``` #### winogrande_xl - Size of downloaded dataset files: 3.40 MB - Size of the generated dataset: 5.58 MB - Total amount of disk used: 8.98 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### winogrande_debiased - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. #### winogrande_l - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. #### winogrande_m - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. #### winogrande_s - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. #### winogrande_xl - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. ### Data Splits \| name \|train\|validation\|test\| \|-------------------\|----:\|---------:\|---:\| \|winogrande_debiased\| 9248\| 1267\|1767\| \|winogrande_l \|10234\| 1267\|1767\| \|winogrande_m \| 2558\| 1267\|1767\| \|winogrande_s \| 640\| 1267\|1767\| \|winogrande_xl \|40398\| 1267\|1767\| \|winogrande_xs \| 160\| 1267\|1767\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{ai2:winogrande, title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale}, authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi }, year={2019} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@TevenLeScao](https://github.com/TevenLeScao), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
gsm8k	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: gsm8k pretty_name: Grade School Math 8K tags: - math-word-problems dataset_info: - config_name: main features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 3963202 num_examples: 7473 - name: test num_bytes: 713732 num_examples: 1319 download_size: 2725633 dataset_size: 4676934 - config_name: socratic features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 5198108 num_examples: 7473 - name: test num_bytes: 936859 num_examples: 1319 download_size: 3164254 dataset_size: 6134967 configs: - config_name: main data_files: - split: train path: main/train-* - split: test path: main/test-* - config_name: socratic data_files: - split: train path: socratic/train-* - split: test path: socratic/test-* --- # Dataset Card for GSM8K ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://openai.com/blog/grade-school-math/ - Repository: https://github.com/openai/grade-school-math - Paper: https://arxiv.org/abs/2110.14168 - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. - These problems take between 2 and 8 steps to solve. - Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. - A bright middle school student should be able to solve every problem: from the paper, "Problems require no concepts beyond the level of early Algebra, and the vast majority of problems can be solved without explicitly defining a variable." - Solutions are provided in natural language, as opposed to pure math expressions. From the paper: "We believe this is the most generally useful data format, and we expect it to shed light on the properties of large language models’ internal monologues"" ### Supported Tasks and Leaderboards This dataset is generally used to test logic and math in language modelling. It has been used for many benchmarks, including the [LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances For the `main` configuration, each instance contains a string for the grade-school level math question and a string for the corresponding answer with multiple steps of reasoning and calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)). ```python { 'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?', 'answer': 'Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72', } ``` For the `socratic` configuration, each instance contains a string for a grade-school level math question, a string for the corresponding answer with multiple steps of reasoning, calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)), and Socratic sub-questions. ```python { 'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?', 'answer': 'How many clips did Natalia sell in May? Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nHow many clips did Natalia sell altogether in April and May? Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72', } ``` ### Data Fields The data fields are the same among `main` and `socratic` configurations and their individual splits. - question: The question string to a grade school math problem. - answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution. ### Data Splits \| name \|train\|validation\| \|--------\|----:\|---------:\| \|main \| 7473\| 1319\| \|socratic\| 7473\| 1319\| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization From the paper, appendix A: > We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? Surge AI (surgehq.ai) ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information ```bibtex @article{cobbe2021gsm8k, title={Training Verifiers to Solve Math Word Problems}, author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John}, journal={arXiv preprint arXiv:2110.14168}, year={2021} } ``` ### Contributions Thanks to [@jon-tow](https://github.com/jon-tow) for adding this dataset.
piqa	--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: piqa pretty_name: 'Physical Interaction: Question Answering' dataset_info: features: - name: goal dtype: string - name: sol1 dtype: string - name: sol2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' config_name: plain_text splits: - name: train num_bytes: 4104026 num_examples: 16113 - name: test num_bytes: 761521 num_examples: 3084 - name: validation num_bytes: 464321 num_examples: 1838 download_size: 2638625 dataset_size: 5329868 --- # Dataset Card for "Physical Interaction: Question Answering" ## 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: [PIQA homepage](https://yonatanbisk.com/piqa/) - Paper: [PIQA: Reasoning about Physical Commonsense in Natural Language](https://arxiv.org/abs/1911.11641) - Leaderboard: [Official leaderboard](https://yonatanbisk.com/piqa/) Note that there is a [2nd leaderboard](https://leaderboard.allenai.org/physicaliqa) featuring a different (blind) test set with 3,446 examples as part of the Machine Commonsense DARPA project. - Point of Contact: [Yonatan Bisk](https://yonatanbisk.com/piqa/) ### Dataset Summary To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Physical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language. PIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials. ### Supported Tasks and Leaderboards The underlying task is formualted as multiple choice question answering: given a question `q` and two possible solutions `s1`, `s2`, a model or a human must choose the most appropriate solution, of which exactly one is correct. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An example looks like this: ``` { "goal": "How do I ready a guinea pig cage for it's new occupants?", "sol1": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped paper strips, you will also need to supply it with a water bottle and a food dish.", "sol2": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped jeans material, you will also need to supply it with a water bottle and a food dish.", "label": 0, } ``` Note that the test set contains no labels. Predictions need to be submitted to the leaderboard. ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. - `goal`: the question which requires physical commonsense to be answered correctly - `sol1`: the first solution - `sol2`: the second solution - `label`: the correct solution. `0` refers to `sol1` and `1` refers to `sol2` ### Data Splits The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing. ## Dataset Creation ### Curation Rationale The goal of the dataset is to construct a resource that requires concrete physical reasoning. ### Source Data The authors provide a prompt to the annotators derived from instructables.com. The instructables website is a crowdsourced collection of instruc- tions for doing everything from cooking to car repair. In most cases, users provide images or videos detailing each step and a list of tools that will be required. Most goals are simultaneously rare and unsurprising. While an annotator is unlikely to have built a UV-Flourescent steampunk lamp or made a backpack out of duct tape, it is not surprising that someone interested in home crafting would create these, nor will the tools and materials be unfamiliar to the average person. Using these examples as the seed for their annotation, helps remind annotators about the less prototypical uses of everyday objects. Second, and equally important, is that instructions build on one another. This means that any QA pair inspired by an instructable is more likely to explicitly state assumptions about what preconditions need to be met to start the task and what postconditions define success. Annotators were asked to glance at the instructions of an instructable and pull out or have it inspire them to construct two component tasks. They would then articulate the goal (often centered on atypical materials) and how to achieve it. In addition, annotaters were asked to provide a permutation to their own solution which makes it invalid (the negative solution), often subtly. #### Initial Data Collection and Normalization During validation, examples with low agreement were removed from the data. The dataset is further cleaned to remove stylistic artifacts and trivial examples from the data, which have been shown to artificially inflate model performance on previous NLI benchmarks.using the AFLite algorithm introduced in ([Sakaguchi et al. 2020](https://arxiv.org/abs/1907.10641); [Sap et al. 2019](https://arxiv.org/abs/1904.09728)) which is an improvement on adversarial filtering ([Zellers et al, 2018](https://arxiv.org/abs/1808.05326)). #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process Annotations are by construction obtained when crowdsourcers complete the prompt. #### Who are the annotators? Paid crowdsourcers ### 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 Unknown ### Citation Information ``` @inproceedings{Bisk2020, author = {Yonatan Bisk and Rowan Zellers and Ronan Le Bras and Jianfeng Gao and Yejin Choi}, title = {PIQA: Reasoning about Physical Commonsense in Natural Language}, booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence}, year = {2020}, } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
lukaemon/bbh	--- 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} } ```
facebook/flores	--- annotations_creators: - found language_creators: - expert-generated language: - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu license: - cc-by-sa-4.0 multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended\|flores task_categories: - text2text-generation - translation task_ids: [] paperswithcode_id: flores pretty_name: flores200 language_details: ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn tags: - conditional-text-generation --- # Dataset Card for Flores 200 ## Table of Contents - [Dataset Card for Flores 200](#dataset-card-for-flores-200) - [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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Home: [Flores](https://github.com/facebookresearch/flores) - Repository: [Github](https://github.com/facebookresearch/flores) ### Dataset Summary FLORES is a benchmark dataset for machine translation between English and low-resource languages. >The creation of FLORES-200 doubles the existing language coverage of FLORES-101. Given the nature of the new languages, which have less standardization and require more specialized professional translations, the verification process became more complex. This required modifications to the translation workflow. FLORES-200 has several languages which were not translated from English. Specifically, several languages were translated from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also includes two script alternatives for four languages. FLORES-200 consists of translations from 842 distinct web articles, totaling 3001 sentences. These sentences are divided into three splits: dev, devtest, and test (hidden). On average, sentences are approximately 21 words long. Disclaimer: *The Flores-200 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). ### Supported Tasks and Leaderboards #### Multilingual Machine Translation Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html). Flores 200 is an extention of this. ### Languages The dataset contains parallel sentences for 200 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) plus an additional code describing the script (e.g., "eng_Latn", "ukr_Cyrl"). See [the webpage for code descriptions](https://github.com/facebookresearch/flores/blob/main/flores200/README.md). Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command. Use a hyphenated pairing to get two langauges in one datapoint (e.g., "eng_Latn-ukr_Cyrl" will provide sentences in the format below). ## Dataset Structure ### Data Instances A sample from the `dev` split for the Ukrainian language (`ukr_Cyrl` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits. ```python { 'id': 1, 'sentence': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.', 'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet', 'domain': 'wikinews', 'topic': 'health', 'has_image': 0, 'has_hyperlink': 0 } ``` When using a hyphenated pairing or using the `all` function, data will be presented as follows: ```python { 'id': 1, 'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet', 'domain': 'wikinews', 'topic': 'health', 'has_image': 0, 'has_hyperlink': 0, 'sentence_eng_Latn': 'On Monday, scientists from the Stanford University School of Medicine announced the invention of a new diagnostic tool that can sort cells by type: a tiny printable chip that can be manufactured using standard inkjet printers for possibly about one U.S. cent each.', 'sentence_ukr_Cyrl': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.' } ``` The text is provided as-in the original dataset, without further preprocessing or tokenization. ### Data Fields - `id`: Row number for the data entry, starting at 1. - `sentence`: The full sentence in the specific language (may have _lang for pairings) - `URL`: The URL for the English article from which the sentence was extracted. - `domain`: The domain of the sentence. - `topic`: The topic of the sentence. - `has_image`: Whether the original article contains an image. - `has_hyperlink`: Whether the sentence contains a hyperlink. ### Data Splits \| config\| `dev`\| `devtest`\| \|-----------------:\|-----:\|---------:\| \|all configurations\| 997\| 1012:\| ### Dataset Creation Please refer to the original article [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) for additional information on dataset creation. ## Additional Information ### Dataset Curators See paper for details. ### Licensing Information Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information Please cite the authors if you use these corpora in your work: ```bibtex @article{nllb2022, author = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang}, title = {No Language Left Behind: Scaling Human-Centered Machine Translation}, year = {2022} } ``` Please also cite prior work that this dataset builds on: ```bibtex @inproceedings{, title={The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation}, author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela}, year={2021} } ``` ```bibtex @inproceedings{, title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English}, author={Guzm\'{a}n, Francisco and Chen, Peng-Jen and Ott, Myle and Pino, Juan and Lample, Guillaume and Koehn, Philipp and Chaudhary, Vishrav and Ranzato, Marc'Aurelio}, journal={arXiv preprint arXiv:1902.01382}, year={2019} } ```
openai_humaneval	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: humaneval pretty_name: OpenAI HumanEval tags: - code-generation dataset_info: config_name: openai_humaneval features: - name: task_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: test dtype: string - name: entry_point dtype: string splits: - name: test num_bytes: 194394 num_examples: 164 download_size: 83920 dataset_size: 194394 configs: - config_name: openai_humaneval data_files: - split: test path: openai_humaneval/test-* default: true --- # Dataset Card for OpenAI HumanEval ## Table of Contents - [OpenAI HumanEval](#openai-humaneval) - [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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [GitHub Repository](https://github.com/openai/human-eval) - Paper: [Evaluating Large Language Models Trained on Code](https://arxiv.org/abs/2107.03374) ### Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models. ### Supported Tasks and Leaderboards ### Languages The programming problems are written in Python and contain English natural text in comments and docstrings. ## Dataset Structure ```python from datasets import load_dataset load_dataset("openai_humaneval") DatasetDict({ test: Dataset({ features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point'], num_rows: 164 }) }) ``` ### Data Instances An example of a dataset instance: ``` { "task_id": "test/0", "prompt": "def return1():\n", "canonical_solution": " return 1", "test": "def check(candidate):\n assert candidate() == 1", "entry_point": "return1" } ``` ### Data Fields - `task_id`: identifier for the data sample - `prompt`: input for the model containing function header and docstrings - `canonical_solution`: solution for the problem in the `prompt` - `test`: contains function to test generated code for correctness - `entry_point`: entry point for test ### Data Splits The dataset only consists of a test split with 164 samples. ## Dataset Creation ### Curation Rationale Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps. ### Source Data The dataset was handcrafted by engineers and researchers at OpenAI. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators OpenAI ### Licensing Information MIT License ### Citation Information ``` @misc{chen2021evaluating, title={Evaluating Large Language Models Trained on Code}, author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, year={2021}, eprint={2107.03374}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ### Contributions Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.

hails/mmlu_no_train

--- 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.

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

--- 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') ```

lighteval/mmlu

--- 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.

cais/mmlu

--- 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 - 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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 - 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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 - 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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.

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

--- 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)

wikitext

--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 - gfdl multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: wikitext-2 pretty_name: WikiText dataset_info: - config_name: wikitext-103-raw-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1305088 num_examples: 4358 - name: train num_bytes: 546500949 num_examples: 1801350 - name: validation num_bytes: 1159288 num_examples: 3760 download_size: 315466397 dataset_size: 548965325 - config_name: wikitext-103-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1295575 num_examples: 4358 - name: train num_bytes: 545141915 num_examples: 1801350 - name: validation num_bytes: 1154751 num_examples: 3760 download_size: 313093838 dataset_size: 547592241 - config_name: wikitext-2-raw-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1305088 num_examples: 4358 - name: train num_bytes: 11061717 num_examples: 36718 - name: validation num_bytes: 1159288 num_examples: 3760 download_size: 7747362 dataset_size: 13526093 - config_name: wikitext-2-v1 features: - name: text dtype: string splits: - name: test num_bytes: 1270947 num_examples: 4358 - name: train num_bytes: 10918118 num_examples: 36718 - name: validation num_bytes: 1134123 num_examples: 3760 download_size: 7371282 dataset_size: 13323188 configs: - config_name: wikitext-103-raw-v1 data_files: - split: test path: wikitext-103-raw-v1/test-* - split: train path: wikitext-103-raw-v1/train-* - split: validation path: wikitext-103-raw-v1/validation-* - config_name: wikitext-103-v1 data_files: - split: test path: wikitext-103-v1/test-* - split: train path: wikitext-103-v1/train-* - split: validation path: wikitext-103-v1/validation-* - config_name: wikitext-2-raw-v1 data_files: - split: test path: wikitext-2-raw-v1/test-* - split: train path: wikitext-2-raw-v1/train-* - split: validation path: wikitext-2-raw-v1/validation-* - config_name: wikitext-2-v1 data_files: - split: test path: wikitext-2-v1/test-* - split: train path: wikitext-2-v1/train-* - split: validation path: wikitext-2-v1/validation-* --- # Dataset Card for "wikitext" ## 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://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843) - **Point of Contact:** [Stephen Merity](mailto:smerity@salesforce.com) - **Size of downloaded dataset files:** 391.41 MB - **Size of the generated dataset:** 1.12 GB - **Total amount of disk used:** 1.52 GB ### Dataset Summary The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger vocabulary and retains the original case, punctuation and numbers - all of which are removed in PTB. As it is composed of full articles, the dataset is well suited for models that can take advantage of long term dependencies. Each subset comes in two different variants: - Raw (for character level work) contain the raw tokens, before the addition of the <unk> (unknown) tokens. - Non-raw (for word level work) contain only the tokens in their vocabulary (wiki.train.tokens, wiki.valid.tokens, and wiki.test.tokens). The out-of-vocabulary tokens have been replaced with the the <unk> token. ### 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 #### wikitext-103-raw-v1 - **Size of downloaded dataset files:** 191.98 MB - **Size of the generated dataset:** 549.42 MB - **Total amount of disk used:** 741.41 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "text": "\" The gold dollar or gold one @-@ dollar piece was a coin struck as a regular issue by the United States Bureau of the Mint from..." } ``` #### wikitext-103-v1 - **Size of downloaded dataset files:** 190.23 MB - **Size of the generated dataset:** 548.05 MB - **Total amount of disk used:** 738.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..." } ``` #### wikitext-2-raw-v1 - **Size of downloaded dataset files:** 4.72 MB - **Size of the generated dataset:** 13.54 MB - **Total amount of disk used:** 18.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" The Sinclair Scientific Programmable was introduced in 1975 , with the same case as the Sinclair Oxford . It was larger than t..." } ``` #### wikitext-2-v1 - **Size of downloaded dataset files:** 4.48 MB - **Size of the generated dataset:** 13.34 MB - **Total amount of disk used:** 17.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..." } ``` ### Data Fields The data fields are the same among all splits. #### wikitext-103-raw-v1 - `text`: a `string` feature. #### wikitext-103-v1 - `text`: a `string` feature. #### wikitext-2-raw-v1 - `text`: a `string` feature. #### wikitext-2-v1 - `text`: a `string` feature. ### Data Splits | name | train |validation|test| |-------------------|------:|---------:|---:| |wikitext-103-raw-v1|1801350| 3760|4358| |wikitext-103-v1 |1801350| 3760|4358| |wikitext-2-raw-v1 | 36718| 3760|4358| |wikitext-2-v1 | 36718| 3760|4358| ## 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 The dataset is available under the [Creative Commons Attribution-ShareAlike License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @misc{merity2016pointer, title={Pointer Sentinel Mixture Models}, author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher}, year={2016}, eprint={1609.07843}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

yelp_review_full

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: YelpReviewFull license_details: yelp-licence dataset_info: config_name: yelp_review_full features: - name: label dtype: class_label: names: '0': 1 star '1': 2 star '2': 3 stars '3': 4 stars '4': 5 stars - name: text dtype: string splits: - name: train num_bytes: 483811554 num_examples: 650000 - name: test num_bytes: 37271188 num_examples: 50000 download_size: 322952369 dataset_size: 521082742 configs: - config_name: yelp_review_full data_files: - split: train path: yelp_review_full/train-* - split: test path: yelp_review_full/test-* default: true train-eval-index: - config: yelp_review_full task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- --- # Dataset Card for YelpReviewFull ## 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:** [Yelp](https://www.yelp.com/dataset) - **Repository:** [Crepe](https://github.com/zhangxiangxiao/Crepe) - **Paper:** [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626) - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu) ### Dataset Summary The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment. ### Languages The reviews were mainly written in english. ## Dataset Structure ### Data Instances A typical data point, comprises of a text and the corresponding label. An example from the YelpReviewFull test set looks as follows: ``` { 'label': 0, 'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!' } ``` ### Data Fields - 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'label': Corresponds to the score associated with the review (between 1 and 5). ### Data Splits The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5. In total there are 650,000 trainig samples and 50,000 testing samples. ## Dataset Creation ### Curation Rationale The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### 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 You can check the official [yelp-dataset-agreement](https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf). ### Citation Information Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.

etechgrid/Prompts_for_Voice_cloning_and_TTS

--- license: mit ---

fsicoli/common_voice_17_0

--- license: cc0-1.0 language: - ab - af - am - ar - as - ast - az - ba - bas - be - bg - bn - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - dyu - el - en - eo - es - et - eu - fa - fi - fr - gl - gn - ha - he - hi - hsb - hu - ia - id - ig - is - it - ja - ka - kab - kk - kmr - ko - ky - lg - lo - lt - lv - mdf - mhr - mk - ml - mn - mr - mrj - mt - myv - nl - oc - or - pl - ps - pt - quy - ro - ru - rw - sah - sat - sc - sk - skr - sl - sq - sr - sw - ta - th - ti - tig - tk - tok - tr - tt - tw - ug - uk - ur - uz - vi - vot - yue - za - zgh - zh - yo task_categories: - automatic-speech-recognition pretty_name: Common Voice Corpus 17.0 size_categories: - 100B<n<1T tags: - mozilla - foundation --- # Dataset Card for Common Voice Corpus 17.0  This dataset is an unofficial version of the Mozilla Common Voice Corpus 17. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/. ## Languages ``` Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba ``` ## How to use The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function. For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese): ``` from datasets import load_dataset cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ``` from datasets import load_dataset cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train", streaming=True) print(next(iter(cv_17))) ``` Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed). ### Local ``` from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train") batch_sampler = BatchSampler(RandomSampler(cv_17), batch_size=32, drop_last=False) dataloader = DataLoader(cv_17, batch_sampler=batch_sampler) ``` ### Streaming ``` from datasets import load_dataset from torch.utils.data import DataLoader cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train") dataloader = DataLoader(cv_17, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets. ### Dataset Structure Data Instances A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment. ### Licensing Information Public Domain, CC-0 ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ---

cifar10

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-10 pretty_name: Cifar10 dataset_info: config_name: plain_text features: - name: img dtype: image - name: label dtype: class_label: names: '0': airplane '1': automobile '2': bird '3': cat '4': deer '5': dog '6': frog '7': horse '8': ship '9': truck splits: - name: train num_bytes: 113648310.0 num_examples: 50000 - name: test num_bytes: 22731580.0 num_examples: 10000 download_size: 143646105 dataset_size: 136379890.0 configs: - config_name: plain_text data_files: - split: train path: plain_text/train-* - split: test path: plain_text/test-* default: true --- # Dataset Card for CIFAR-10 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.cs.toronto.edu/~kriz/cifar.html - **Repository:** - **Paper:** Learning Multiple Layers of Features from Tiny Images by Alex Krizhevsky - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-10). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x201FA6EE748>, 'label': 0 } ``` ### Data Fields - img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - label: 0-9 with the following correspondence 0 airplane 1 automobile 2 bird 3 cat 4 deer 5 dog 6 frog 7 horse 8 ship 9 truck ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.

cifar100

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-100 pretty_name: Cifar100 dataset_info: config_name: cifar100 features: - name: img dtype: image - name: fine_label dtype: class_label: names: '0': apple '1': aquarium_fish '2': baby '3': bear '4': beaver '5': bed '6': bee '7': beetle '8': bicycle '9': bottle '10': bowl '11': boy '12': bridge '13': bus '14': butterfly '15': camel '16': can '17': castle '18': caterpillar '19': cattle '20': chair '21': chimpanzee '22': clock '23': cloud '24': cockroach '25': couch '26': cra '27': crocodile '28': cup '29': dinosaur '30': dolphin '31': elephant '32': flatfish '33': forest '34': fox '35': girl '36': hamster '37': house '38': kangaroo '39': keyboard '40': lamp '41': lawn_mower '42': leopard '43': lion '44': lizard '45': lobster '46': man '47': maple_tree '48': motorcycle '49': mountain '50': mouse '51': mushroom '52': oak_tree '53': orange '54': orchid '55': otter '56': palm_tree '57': pear '58': pickup_truck '59': pine_tree '60': plain '61': plate '62': poppy '63': porcupine '64': possum '65': rabbit '66': raccoon '67': ray '68': road '69': rocket '70': rose '71': sea '72': seal '73': shark '74': shrew '75': skunk '76': skyscraper '77': snail '78': snake '79': spider '80': squirrel '81': streetcar '82': sunflower '83': sweet_pepper '84': table '85': tank '86': telephone '87': television '88': tiger '89': tractor '90': train '91': trout '92': tulip '93': turtle '94': wardrobe '95': whale '96': willow_tree '97': wolf '98': woman '99': worm - name: coarse_label dtype: class_label: names: '0': aquatic_mammals '1': fish '2': flowers '3': food_containers '4': fruit_and_vegetables '5': household_electrical_devices '6': household_furniture '7': insects '8': large_carnivores '9': large_man-made_outdoor_things '10': large_natural_outdoor_scenes '11': large_omnivores_and_herbivores '12': medium_mammals '13': non-insect_invertebrates '14': people '15': reptiles '16': small_mammals '17': trees '18': vehicles_1 '19': vehicles_2 splits: - name: train num_bytes: 112545106.0 num_examples: 50000 - name: test num_bytes: 22564261.0 num_examples: 10000 download_size: 142291368 dataset_size: 135109367.0 configs: - config_name: cifar100 data_files: - split: train path: cifar100/train-* - split: test path: cifar100/test-* default: true --- # Dataset Card for CIFAR-100 ## 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:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html) - **Repository:** - **Paper:** [Paper](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass). ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-100). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19, 'coarse_label': 11 } ``` ### Data Fields - `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `fine_label`: an `int` classification label with the following mapping: `0`: apple `1`: aquarium_fish `2`: baby `3`: bear `4`: beaver `5`: bed `6`: bee `7`: beetle `8`: bicycle `9`: bottle `10`: bowl `11`: boy `12`: bridge `13`: bus `14`: butterfly `15`: camel `16`: can `17`: castle `18`: caterpillar `19`: cattle `20`: chair `21`: chimpanzee `22`: clock `23`: cloud `24`: cockroach `25`: couch `26`: cra `27`: crocodile `28`: cup `29`: dinosaur `30`: dolphin `31`: elephant `32`: flatfish `33`: forest `34`: fox `35`: girl `36`: hamster `37`: house `38`: kangaroo `39`: keyboard `40`: lamp `41`: lawn_mower `42`: leopard `43`: lion `44`: lizard `45`: lobster `46`: man `47`: maple_tree `48`: motorcycle `49`: mountain `50`: mouse `51`: mushroom `52`: oak_tree `53`: orange `54`: orchid `55`: otter `56`: palm_tree `57`: pear `58`: pickup_truck `59`: pine_tree `60`: plain `61`: plate `62`: poppy `63`: porcupine `64`: possum `65`: rabbit `66`: raccoon `67`: ray `68`: road `69`: rocket `70`: rose `71`: sea `72`: seal `73`: shark `74`: shrew `75`: skunk `76`: skyscraper `77`: snail `78`: snake `79`: spider `80`: squirrel `81`: streetcar `82`: sunflower `83`: sweet_pepper `84`: table `85`: tank `86`: telephone `87`: television `88`: tiger `89`: tractor `90`: train `91`: trout `92`: tulip `93`: turtle `94`: wardrobe `95`: whale `96`: willow_tree `97`: wolf `98`: woman `99`: worm - `coarse_label`: an `int` coarse classification label with following mapping: `0`: aquatic_mammals `1`: fish `2`: flowers `3`: food_containers `4`: fruit_and_vegetables `5`: household_electrical_devices `6`: household_furniture `7`: insects `8`: large_carnivores `9`: large_man-made_outdoor_things `10`: large_natural_outdoor_scenes `11`: large_omnivores_and_herbivores `12`: medium_mammals `13`: non-insect_invertebrates `14`: people `15`: reptiles `16`: small_mammals `17`: trees `18`: vehicles_1 `19`: vehicles_2 ### Data Splits | name |train|test| |----------|----:|---------:| |cifar100|50000| 10000| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.

winogrande

--- language: - en paperswithcode_id: winogrande pretty_name: WinoGrande dataset_info: - config_name: winogrande_xs features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 20704 num_examples: 160 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 412552 - config_name: winogrande_s features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 82308 num_examples: 640 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 474156 - config_name: winogrande_m features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 329001 num_examples: 2558 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 720849 - config_name: winogrande_l features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1319576 num_examples: 10234 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 1711424 - config_name: winogrande_xl features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 5185832 num_examples: 40398 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 5577680 - config_name: winogrande_debiased features: - name: sentence dtype: string - name: option1 dtype: string - name: option2 dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1203420 num_examples: 9248 - name: test num_bytes: 227649 num_examples: 1767 - name: validation num_bytes: 164199 num_examples: 1267 download_size: 3395492 dataset_size: 1595268 --- # Dataset Card for "winogrande" ## 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://leaderboard.allenai.org/winogrande/submissions/get-started](https://leaderboard.allenai.org/winogrande/submissions/get-started) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 20.37 MB - **Size of the generated dataset:** 10.50 MB - **Total amount of disk used:** 30.87 MB ### Dataset Summary WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires commonsense reasoning. ### 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 #### winogrande_debiased - **Size of downloaded dataset files:** 3.40 MB - **Size of the generated dataset:** 1.59 MB - **Total amount of disk used:** 4.99 MB An example of 'train' looks as follows. ``` ``` #### winogrande_l - **Size of downloaded dataset files:** 3.40 MB - **Size of the generated dataset:** 1.71 MB - **Total amount of disk used:** 5.11 MB An example of 'validation' looks as follows. ``` ``` #### winogrande_m - **Size of downloaded dataset files:** 3.40 MB - **Size of the generated dataset:** 0.72 MB - **Total amount of disk used:** 4.12 MB An example of 'validation' looks as follows. ``` ``` #### winogrande_s - **Size of downloaded dataset files:** 3.40 MB - **Size of the generated dataset:** 0.47 MB - **Total amount of disk used:** 3.87 MB An example of 'validation' looks as follows. ``` ``` #### winogrande_xl - **Size of downloaded dataset files:** 3.40 MB - **Size of the generated dataset:** 5.58 MB - **Total amount of disk used:** 8.98 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### winogrande_debiased - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. #### winogrande_l - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. #### winogrande_m - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. #### winogrande_s - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. #### winogrande_xl - `sentence`: a `string` feature. - `option1`: a `string` feature. - `option2`: a `string` feature. - `answer`: a `string` feature. ### Data Splits | name |train|validation|test| |-------------------|----:|---------:|---:| |winogrande_debiased| 9248| 1267|1767| |winogrande_l |10234| 1267|1767| |winogrande_m | 2558| 1267|1767| |winogrande_s | 640| 1267|1767| |winogrande_xl |40398| 1267|1767| |winogrande_xs | 160| 1267|1767| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{ai2:winogrande, title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale}, authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi }, year={2019} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@TevenLeScao](https://github.com/TevenLeScao), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

gsm8k

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: gsm8k pretty_name: Grade School Math 8K tags: - math-word-problems dataset_info: - config_name: main features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 3963202 num_examples: 7473 - name: test num_bytes: 713732 num_examples: 1319 download_size: 2725633 dataset_size: 4676934 - config_name: socratic features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 5198108 num_examples: 7473 - name: test num_bytes: 936859 num_examples: 1319 download_size: 3164254 dataset_size: 6134967 configs: - config_name: main data_files: - split: train path: main/train-* - split: test path: main/test-* - config_name: socratic data_files: - split: train path: socratic/train-* - split: test path: socratic/test-* --- # Dataset Card for GSM8K ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://openai.com/blog/grade-school-math/ - **Repository:** https://github.com/openai/grade-school-math - **Paper:** https://arxiv.org/abs/2110.14168 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. - These problems take between 2 and 8 steps to solve. - Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. - A bright middle school student should be able to solve every problem: from the paper, "Problems require no concepts beyond the level of early Algebra, and the vast majority of problems can be solved without explicitly defining a variable." - Solutions are provided in natural language, as opposed to pure math expressions. From the paper: "We believe this is the most generally useful data format, and we expect it to shed light on the properties of large language models’ internal monologues"" ### Supported Tasks and Leaderboards This dataset is generally used to test logic and math in language modelling. It has been used for many benchmarks, including the [LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances For the `main` configuration, each instance contains a string for the grade-school level math question and a string for the corresponding answer with multiple steps of reasoning and calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)). ```python { 'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?', 'answer': 'Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72', } ``` For the `socratic` configuration, each instance contains a string for a grade-school level math question, a string for the corresponding answer with multiple steps of reasoning, calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)), and *Socratic sub-questions*. ```python { 'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?', 'answer': 'How many clips did Natalia sell in May? ** Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nHow many clips did Natalia sell altogether in April and May? ** Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72', } ``` ### Data Fields The data fields are the same among `main` and `socratic` configurations and their individual splits. - question: The question string to a grade school math problem. - answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution. ### Data Splits | name |train|validation| |--------|----:|---------:| |main | 7473| 1319| |socratic| 7473| 1319| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization From the paper, appendix A: > We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? Surge AI (surgehq.ai) ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information ```bibtex @article{cobbe2021gsm8k, title={Training Verifiers to Solve Math Word Problems}, author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John}, journal={arXiv preprint arXiv:2110.14168}, year={2021} } ``` ### Contributions Thanks to [@jon-tow](https://github.com/jon-tow) for adding this dataset.

piqa

--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: piqa pretty_name: 'Physical Interaction: Question Answering' dataset_info: features: - name: goal dtype: string - name: sol1 dtype: string - name: sol2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' config_name: plain_text splits: - name: train num_bytes: 4104026 num_examples: 16113 - name: test num_bytes: 761521 num_examples: 3084 - name: validation num_bytes: 464321 num_examples: 1838 download_size: 2638625 dataset_size: 5329868 --- # Dataset Card for "Physical Interaction: Question Answering" ## 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:** [PIQA homepage](https://yonatanbisk.com/piqa/) - **Paper:** [PIQA: Reasoning about Physical Commonsense in Natural Language](https://arxiv.org/abs/1911.11641) - **Leaderboard:** [Official leaderboard](https://yonatanbisk.com/piqa/) *Note that there is a [2nd leaderboard](https://leaderboard.allenai.org/physicaliqa) featuring a different (blind) test set with 3,446 examples as part of the Machine Commonsense DARPA project.* - **Point of Contact:** [Yonatan Bisk](https://yonatanbisk.com/piqa/) ### Dataset Summary *To apply eyeshadow without a brush, should I use a cotton swab or a toothpick?* Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Physical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language. PIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials. ### Supported Tasks and Leaderboards The underlying task is formualted as multiple choice question answering: given a question `q` and two possible solutions `s1`, `s2`, a model or a human must choose the most appropriate solution, of which exactly one is correct. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances An example looks like this: ``` { "goal": "How do I ready a guinea pig cage for it's new occupants?", "sol1": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped paper strips, you will also need to supply it with a water bottle and a food dish.", "sol2": "Provide the guinea pig with a cage full of a few inches of bedding made of ripped jeans material, you will also need to supply it with a water bottle and a food dish.", "label": 0, } ``` Note that the test set contains no labels. Predictions need to be submitted to the leaderboard. ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. - `goal`: the question which requires physical commonsense to be answered correctly - `sol1`: the first solution - `sol2`: the second solution - `label`: the correct solution. `0` refers to `sol1` and `1` refers to `sol2` ### Data Splits The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing. ## Dataset Creation ### Curation Rationale The goal of the dataset is to construct a resource that requires concrete physical reasoning. ### Source Data The authors provide a prompt to the annotators derived from instructables.com. The instructables website is a crowdsourced collection of instruc- tions for doing everything from cooking to car repair. In most cases, users provide images or videos detailing each step and a list of tools that will be required. Most goals are simultaneously rare and unsurprising. While an annotator is unlikely to have built a UV-Flourescent steampunk lamp or made a backpack out of duct tape, it is not surprising that someone interested in home crafting would create these, nor will the tools and materials be unfamiliar to the average person. Using these examples as the seed for their annotation, helps remind annotators about the less prototypical uses of everyday objects. Second, and equally important, is that instructions build on one another. This means that any QA pair inspired by an instructable is more likely to explicitly state assumptions about what preconditions need to be met to start the task and what postconditions define success. Annotators were asked to glance at the instructions of an instructable and pull out or have it inspire them to construct two component tasks. They would then articulate the goal (often centered on atypical materials) and how to achieve it. In addition, annotaters were asked to provide a permutation to their own solution which makes it invalid (the negative solution), often subtly. #### Initial Data Collection and Normalization During validation, examples with low agreement were removed from the data. The dataset is further cleaned to remove stylistic artifacts and trivial examples from the data, which have been shown to artificially inflate model performance on previous NLI benchmarks.using the AFLite algorithm introduced in ([Sakaguchi et al. 2020](https://arxiv.org/abs/1907.10641); [Sap et al. 2019](https://arxiv.org/abs/1904.09728)) which is an improvement on adversarial filtering ([Zellers et al, 2018](https://arxiv.org/abs/1808.05326)). #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process Annotations are by construction obtained when crowdsourcers complete the prompt. #### Who are the annotators? Paid crowdsourcers ### 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 Unknown ### Citation Information ``` @inproceedings{Bisk2020, author = {Yonatan Bisk and Rowan Zellers and Ronan Le Bras and Jianfeng Gao and Yejin Choi}, title = {PIQA: Reasoning about Physical Commonsense in Natural Language}, booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence}, year = {2020}, } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

lukaemon/bbh

--- 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} } ```

facebook/flores

--- annotations_creators: - found language_creators: - expert-generated language: - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu license: - cc-by-sa-4.0 multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended|flores task_categories: - text2text-generation - translation task_ids: [] paperswithcode_id: flores pretty_name: flores200 language_details: ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn tags: - conditional-text-generation --- # Dataset Card for Flores 200 ## Table of Contents - [Dataset Card for Flores 200](#dataset-card-for-flores-200) - [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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Home:** [Flores](https://github.com/facebookresearch/flores) - **Repository:** [Github](https://github.com/facebookresearch/flores) ### Dataset Summary FLORES is a benchmark dataset for machine translation between English and low-resource languages. >The creation of FLORES-200 doubles the existing language coverage of FLORES-101. Given the nature of the new languages, which have less standardization and require more specialized professional translations, the verification process became more complex. This required modifications to the translation workflow. FLORES-200 has several languages which were not translated from English. Specifically, several languages were translated from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also includes two script alternatives for four languages. FLORES-200 consists of translations from 842 distinct web articles, totaling 3001 sentences. These sentences are divided into three splits: dev, devtest, and test (hidden). On average, sentences are approximately 21 words long. **Disclaimer**: *The Flores-200 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). ### Supported Tasks and Leaderboards #### Multilingual Machine Translation Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html). Flores 200 is an extention of this. ### Languages The dataset contains parallel sentences for 200 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) plus an additional code describing the script (e.g., "eng_Latn", "ukr_Cyrl"). See [the webpage for code descriptions](https://github.com/facebookresearch/flores/blob/main/flores200/README.md). Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command. Use a hyphenated pairing to get two langauges in one datapoint (e.g., "eng_Latn-ukr_Cyrl" will provide sentences in the format below). ## Dataset Structure ### Data Instances A sample from the `dev` split for the Ukrainian language (`ukr_Cyrl` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits. ```python { 'id': 1, 'sentence': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.', 'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet', 'domain': 'wikinews', 'topic': 'health', 'has_image': 0, 'has_hyperlink': 0 } ``` When using a hyphenated pairing or using the `all` function, data will be presented as follows: ```python { 'id': 1, 'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet', 'domain': 'wikinews', 'topic': 'health', 'has_image': 0, 'has_hyperlink': 0, 'sentence_eng_Latn': 'On Monday, scientists from the Stanford University School of Medicine announced the invention of a new diagnostic tool that can sort cells by type: a tiny printable chip that can be manufactured using standard inkjet printers for possibly about one U.S. cent each.', 'sentence_ukr_Cyrl': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.' } ``` The text is provided as-in the original dataset, without further preprocessing or tokenization. ### Data Fields - `id`: Row number for the data entry, starting at 1. - `sentence`: The full sentence in the specific language (may have _lang for pairings) - `URL`: The URL for the English article from which the sentence was extracted. - `domain`: The domain of the sentence. - `topic`: The topic of the sentence. - `has_image`: Whether the original article contains an image. - `has_hyperlink`: Whether the sentence contains a hyperlink. ### Data Splits | config| `dev`| `devtest`| |-----------------:|-----:|---------:| |all configurations| 997| 1012:| ### Dataset Creation Please refer to the original article [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) for additional information on dataset creation. ## Additional Information ### Dataset Curators See paper for details. ### Licensing Information Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information Please cite the authors if you use these corpora in your work: ```bibtex @article{nllb2022, author = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang}, title = {No Language Left Behind: Scaling Human-Centered Machine Translation}, year = {2022} } ``` Please also cite prior work that this dataset builds on: ```bibtex @inproceedings{, title={The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation}, author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela}, year={2021} } ``` ```bibtex @inproceedings{, title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English}, author={Guzm\'{a}n, Francisco and Chen, Peng-Jen and Ott, Myle and Pino, Juan and Lample, Guillaume and Koehn, Philipp and Chaudhary, Vishrav and Ranzato, Marc'Aurelio}, journal={arXiv preprint arXiv:1902.01382}, year={2019} } ```

openai_humaneval

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: humaneval pretty_name: OpenAI HumanEval tags: - code-generation dataset_info: config_name: openai_humaneval features: - name: task_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: test dtype: string - name: entry_point dtype: string splits: - name: test num_bytes: 194394 num_examples: 164 download_size: 83920 dataset_size: 194394 configs: - config_name: openai_humaneval data_files: - split: test path: openai_humaneval/test-* default: true --- # Dataset Card for OpenAI HumanEval ## Table of Contents - [OpenAI HumanEval](#openai-humaneval) - [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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [GitHub Repository](https://github.com/openai/human-eval) - **Paper:** [Evaluating Large Language Models Trained on Code](https://arxiv.org/abs/2107.03374) ### Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models. ### Supported Tasks and Leaderboards ### Languages The programming problems are written in Python and contain English natural text in comments and docstrings. ## Dataset Structure ```python from datasets import load_dataset load_dataset("openai_humaneval") DatasetDict({ test: Dataset({ features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point'], num_rows: 164 }) }) ``` ### Data Instances An example of a dataset instance: ``` { "task_id": "test/0", "prompt": "def return1():\n", "canonical_solution": " return 1", "test": "def check(candidate):\n assert candidate() == 1", "entry_point": "return1" } ``` ### Data Fields - `task_id`: identifier for the data sample - `prompt`: input for the model containing function header and docstrings - `canonical_solution`: solution for the problem in the `prompt` - `test`: contains function to test generated code for correctness - `entry_point`: entry point for test ### Data Splits The dataset only consists of a test split with 164 samples. ## Dataset Creation ### Curation Rationale Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps. ### Source Data The dataset was handcrafted by engineers and researchers at OpenAI. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators OpenAI ### Licensing Information MIT License ### Citation Information ``` @misc{chen2021evaluating, title={Evaluating Large Language Models Trained on Code}, author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, year={2021}, eprint={2107.03374}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ### Contributions Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.