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"""SQUAD: The Stanford Question Answering Dataset.""" |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{miller2020effect, |
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author = {J. Miller and K. Krauth and B. Recht and L. Schmidt}, |
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booktitle = {International Conference on Machine Learning (ICML)}, |
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title = {The Effect of Natural Distribution Shift on Question Answering Models}, |
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year = {2020}, |
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} |
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""" |
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_DESCRIPTION = r"""\ |
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SquadShifts consists of four new test sets for the Stanford Question Answering \ |
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Dataset (SQuAD) from four different domains: Wikipedia articles, New York \ |
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Times articles, Reddit comments, and Amazon product reviews. Each dataset \ |
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was generated using the same data generating pipeline, Amazon Mechanical \ |
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Turk interface, and data cleaning code as the original SQuAD v1.1 dataset. \ |
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The "new-wikipedia" dataset measures overfitting on the original SQuAD v1.1 \ |
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dataset. The "new-york-times", "reddit", and "amazon" datasets measure \ |
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robustness to natural distribution shifts. We encourage SQuAD model developers \ |
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to also evaluate their methods on these new datasets! \ |
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""" |
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_URL = "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/" |
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_URLS = { |
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"new_wiki": _URL + "new_wiki_v1.0.json", |
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"nyt": _URL + "nyt_v1.0.json", |
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"reddit": _URL + "reddit_v1.0.json", |
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"amazon": _URL + "amazon_reviews_v1.0.json", |
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} |
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class SquadShiftsConfig(datasets.BuilderConfig): |
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"""BuilderConfig for SquadShifts.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for SQUAD. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(SquadShiftsConfig, self).__init__(**kwargs) |
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class SquadShifts(datasets.GeneratorBasedBuilder): |
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"""SquadShifts consists of four new test sets for the SQUAD dataset.""" |
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BUILDER_CONFIGS = [ |
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SquadShiftsConfig( |
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name="new_wiki", |
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version=datasets.Version("1.0.0", ""), |
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description="SQuADShifts New Wikipedia article dataset", |
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), |
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SquadShiftsConfig( |
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name="nyt", |
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version=datasets.Version("1.0.0", ""), |
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description="SQuADShifts New York Times article dataset.", |
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), |
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SquadShiftsConfig( |
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name="reddit", |
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version=datasets.Version("1.0.0", ""), |
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description="SQuADShifts Reddit comment dataset.", |
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), |
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SquadShiftsConfig( |
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name="amazon", |
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version=datasets.Version("1.0.0", ""), |
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description="SQuADShifts Amazon product review dataset.", |
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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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} |
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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://modestyachts.github.io/squadshifts-website/index.html", |
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citation=_CITATION, |
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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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if self.config.name == "new_wiki" or self.config.name == "default": |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["new_wiki"]} |
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), |
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] |
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elif self.config.name == "nyt": |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["nyt"]}), |
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] |
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elif self.config.name == "reddit": |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["reddit"]}), |
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] |
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elif self.config.name == "amazon": |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["amazon"]}), |
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] |
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else: |
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raise ValueError(f"SQuADShifts dataset name {self.config.name} not found!") |
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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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squad = json.load(f) |
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for article in squad["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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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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"text": answers, |
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}, |
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} |
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