"""TODO(commonsense_qa): Add a description here.""" from __future__ import absolute_import, division, print_function import json import os import datasets # TODO(commonsense_qa): BibTeX citation _CITATION = """\ @InProceedings{commonsense_QA, title={COMMONSENSEQA: A Question Answering Challenge Targeting Commonsense Knowledge}, author={Alon, Talmor and Jonathan, Herzig and Nicholas, Lourie and Jonathan ,Berant}, journal={arXiv preprint arXiv:1811.00937v2}, year={2019} """ # TODO(commonsense_qa): _DESCRIPTION = """\ CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers. The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation split, and "Question token split", see paper for details. """ _URL = "https://s3.amazonaws.com/commensenseqa" _TRAINING_FILE = "train_rand_split.jsonl" _DEV_FILE = "dev_rand_split.jsonl" _TEST_FILE = "test_rand_split_no_answers.jsonl" class CommonsenseQa(datasets.GeneratorBasedBuilder): """TODO(commonsense_qa): Short description of my dataset.""" # TODO(commonsense_qa): Set up version. VERSION = datasets.Version("0.1.0") def _info(self): # These are the features of your dataset like images, labels ... features = datasets.Features( { "answerKey": datasets.Value("string"), "question": datasets.Value("string"), "choices": datasets.features.Sequence( { "label": datasets.Value("string"), "text": datasets.Value("string"), } ), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # datasets.features.FeatureConnectors features=features, # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage="https://www.tau-datasets.org/commonsenseqa", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" download_urls = { "train": os.path.join(_URL, _TRAINING_FILE), "test": os.path.join(_URL, _TEST_FILE), "dev": os.path.join(_URL, _DEV_FILE), } downloaded_files = dl_manager.download_and_extract(download_urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"], "split": "train"} ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": downloaded_files["dev"], "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": downloaded_files["test"], "split": "test", }, ), ] def _generate_examples(self, filepath, split): """Yields examples.""" # TODO(commonsense_qa): Yields (key, example) tuples from the dataset with open(filepath, encoding="utf-8") as f: for id_, row in enumerate(f): data = json.loads(row) question = data["question"] choices = question["choices"] labels = [label["label"] for label in choices] texts = [text["text"] for text in choices] stem = question["stem"] if split == "test": answerkey = "" else: answerkey = data["answerKey"] yield id_, { "answerKey": answerkey, "question": stem, "choices": {"label": labels, "text": texts}, }