Delete pquad_public.py
Browse files- pquad_public.py +0 -94
pquad_public.py
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import json
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import datasets
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_CITATION = """\
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@article{darvishi2022pquad,
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title={PQuAD: A Persian Question Answering Dataset},
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author={Darvishi, Kasra and Shahbodagh, Newsha and Abbasiantaeb, Zahra and Momtazi, Saeedeh},
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journal={arXiv preprint arXiv:2202.06219},
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year={2022}
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}
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"""
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_DESCRIPTION = """\\\\
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ParSQuAD: Persian Question Answering Dataset based on Machine Translation of SQuAD 2.0
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"""
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_URL = "https://raw.githubusercontent.com/vassef/pquad_public/main/"
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_URLS = {
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"train": _URL + "train_samples.json",
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"validation":_URL + "validation_samples.json",
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"test": _URL + "test_samples.json",
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}
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class pquad_public_Config(datasets.BuilderConfig):
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"""BuilderConfig for PQuAD."""
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def __init__(self, **kwargs):
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"""BuilderConfig for PQuAD.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(pquad_public_Config, self).__init__(**kwargs)
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class pquad_public(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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pquad_public_Config(name="pquad_public", version=datasets.Version("1.0.0"), description="PQuAD plaint text version 2"),
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]
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def _info(self):
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# datasets.features.FeatureConnectors
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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 of the dataset for documentation
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homepage="https://github.com/vassef/pquad_public/",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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# TODO(persian_qa): Downloads the data and defines the splits
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# dl_manager is a datasets.download.DownloadManager that can be used to
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# download and extract URLs
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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.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
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]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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# TODO(persian_qa): Yields (key, example) tuples from the dataset
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with open(filepath, encoding="utf-8") as f:
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print(filepath)
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squad = json.load(f)
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for example in squad["data"]:
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title = example.get("title", "").strip()
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for paragraph in example["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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# Features currently used are "context", "question", and "answers".
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# Others are extracted here for the ease of future expansions.
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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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