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"""QnAData Question Answering Dataset""" |
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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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_CITATION = """\ |
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a |
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""" |
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_DESCRIPTION = """\ |
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a |
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""" |
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_URL = "https://raw.githubusercontent.com/Gokcimen/Home_Appliance_Dataset/master/" |
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_URLS = { |
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"train": _URL + "train.json", |
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"test": _URL + "test.json", |
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"dev": _URL + "dev.json", |
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} |
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class QnADataConfig(datasets.BuilderConfig): |
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"""BuilderConfig for QnAData.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for QnAData. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(QnADataConfig, self).__init__(**kwargs) |
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class QnAData(datasets.GeneratorBasedBuilder): |
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"""The QnAData Question Answering Dataset. Version 1.0.""" |
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BUILDER_CONFIGS = [ |
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QnADataConfig( |
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name="plain_text", |
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version=datasets.Version("1.0.0"), |
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description="Plain text", |
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), |
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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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"answer_end": 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://raw.githubusercontent.com/Gokcimen/Home_Appliance_Dataset/master/train.json", |
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citation=_CITATION, |
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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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urls_to_download = _URLS |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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] |
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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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with open(filepath, encoding="utf-8") as f: |
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dataset = json.load(f) |
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for article in dataset["data"]: |
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title = article.get("title", "").strip() |
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for paragraph in article["paragraphs"]: |
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context = paragraph["context"].strip() |
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for qa in paragraph["qas"]: |
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question = qa["question"].strip() |
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id_ = qa["id"] |
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answer_starts = [answer["answer_start"] for answer in qa["answers"]] |
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answer_end = [answer["answer_end"] for answer in qa["answers"]] |
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answers = [answer["text"].strip() for answer in qa["answers"]] |
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yield id_, { |
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"title": title, |
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"context": context, |
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"question": question, |
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"id": id_, |
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"answers": { |
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"answer_start": answer_starts, |
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"answer_end": answer_end, |
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"text": answers, |
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}, |
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} |
