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import json |
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import datasets |
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from datasets.tasks import QuestionAnsweringExtractive |
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logger = datasets.logging.get_logger(__name__) |
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_VERSION = "1.0.0" |
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_NAME = "test" |
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_DESCRIPTION = """QA pairs generated in https://aclanthology.org/P18-1177/""" |
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_CITATION = "" |
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_BASE_URL = "https://huggingface.co/datasets/DrakuTheDragon/Test/resolve/main/" |
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_URLS = { |
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str("0_train"): f'{_BASE_URL}/0_wiki_train.json', |
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str("1_train"): f'{_BASE_URL}/1_wiki_train.json', |
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str("2_train"): f'{_BASE_URL}/2_wiki_train.json', |
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str("3_train"): f'{_BASE_URL}/3_wiki_train.json', |
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str("4_train"): f'{_BASE_URL}/4_wiki_train.json', |
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str("5_train"): f'{_BASE_URL}/5_wiki_train.json', |
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str("6_train"): f'{_BASE_URL}/6_wiki_train.json', |
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str("7_train"): f'{_BASE_URL}/7_wiki_train.json', |
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str("8_train"): f'{_BASE_URL}/8_wiki_train.json', |
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str("9_train"): f'{_BASE_URL}/9_wiki_train.json', |
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} |
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class QAHarvestingFromWikipediaConfig(datasets.BuilderConfig): |
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"""BuilderConfig""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(QAHarvestingFromWikipediaConfig, self).__init__(**kwargs) |
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class QAHarvestingFromWikipedia(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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QAHarvestingFromWikipediaConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"context": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answers": datasets.features.Sequence( |
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{ |
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"text": datasets.Value("string"), |
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"answer_start": datasets.Value("int32"), |
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} |
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), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/asahi417/lm-question-generation", |
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task_templates=[ |
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QuestionAnsweringExtractive( |
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question_column="question", context_column="context", answers_column="answers" |
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) |
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], |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_file = dl_manager.download_and_extract(_URLS) |
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return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_file[str(i)]}) |
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for i in ["0_train","1_train","2_train","3_train","4_train","5_train","6_train","7_train","8_train","9_train"]] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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key = 0 |
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with open(filepath, encoding="utf-8") as f: |
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squad = json.load(f) |
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for article in squad["data"]: |
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title = article.get("title", "") |
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for paragraph in article["paragraphs"]: |
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context = paragraph["context"] |
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for qa in paragraph["qas"]: |
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answer_starts = [answer["answer_start"] for answer in qa["answers"]] |
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answers = [answer["text"] for answer in qa["answers"]] |
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yield key, { |
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"title": title, |
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"context": context, |
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"question": qa["question"], |
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"id": qa["id"], |
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"answers": { |
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"answer_start": answer_starts, |
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"text": answers, |
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}, |
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} |
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key += 1 |
