import json import datasets from datasets.tasks import QuestionAnsweringExtractive logger = datasets.logging.get_logger(__name__) _VERSION = "1.0.0" _NAME = "test" _DESCRIPTION = """QA pairs generated in https://aclanthology.org/P18-1177/""" _CITATION = "" _BASE_URL = "https://huggingface.co/datasets/DrakuTheDragon/Test/resolve/main/" _URLS = { str("0_train"): f'{_BASE_URL}/0_wiki_train.json', str("1_train"): f'{_BASE_URL}/1_wiki_train.json', str("2_train"): f'{_BASE_URL}/2_wiki_train.json', str("3_train"): f'{_BASE_URL}/3_wiki_train.json', str("4_train"): f'{_BASE_URL}/4_wiki_train.json', str("5_train"): f'{_BASE_URL}/5_wiki_train.json', str("6_train"): f'{_BASE_URL}/6_wiki_train.json', str("7_train"): f'{_BASE_URL}/7_wiki_train.json', str("8_train"): f'{_BASE_URL}/8_wiki_train.json', str("9_train"): f'{_BASE_URL}/9_wiki_train.json', } class QAHarvestingFromWikipediaConfig(datasets.BuilderConfig): """BuilderConfig""" def __init__(self, **kwargs): """BuilderConfig Args: **kwargs: keyword arguments forwarded to super. """ super(QAHarvestingFromWikipediaConfig, self).__init__(**kwargs) class QAHarvestingFromWikipedia(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ QAHarvestingFromWikipediaConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "title": datasets.Value("string"), "context": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.features.Sequence( { "text": datasets.Value("string"), "answer_start": datasets.Value("int32"), } ), } ), supervised_keys=None, homepage="https://github.com/asahi417/lm-question-generation", task_templates=[ QuestionAnsweringExtractive( question_column="question", context_column="context", answers_column="answers" ) ], ) def _split_generators(self, dl_manager): downloaded_file = dl_manager.download_and_extract(_URLS) return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_file[str(i)]}) for i in ["0_train","1_train","2_train","3_train","4_train","5_train","6_train","7_train","8_train","9_train"]] def _generate_examples(self, filepath): """This function returns the examples in the raw (text) form.""" logger.info("generating examples from = %s", filepath) key = 0 with open(filepath, encoding="utf-8") as f: squad = json.load(f) for article in squad["data"]: title = article.get("title", "") for paragraph in article["paragraphs"]: context = paragraph["context"] # do not strip leading blank spaces GH-2585 for qa in paragraph["qas"]: answer_starts = [answer["answer_start"] for answer in qa["answers"]] answers = [answer["text"] for answer in qa["answers"]] # Features currently used are "context", "question", and "answers". # Others are extracted here for the ease of future expansions. yield key, { "title": title, "context": context, "question": qa["question"], "id": qa["id"], "answers": { "answer_start": answer_starts, "text": answers, }, } key += 1