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"""TODO(squad_it): Add a description here.""" |
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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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_CITATION = """\ |
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@InProceedings{10.1007/978-3-030-03840-3_29, |
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author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto}, |
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editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo", |
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title={Neural Learning for Question Answering in Italian}, |
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booktitle={AI*IA 2018 -- Advances in Artificial Intelligence}, |
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year={2018}, |
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publisher={Springer International Publishing}, |
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address={Cham}, |
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pages={389--402}, |
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isbn={978-3-030-03840-3} |
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} |
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""" |
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_DESCRIPTION = """\ |
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SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset |
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into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. |
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The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is |
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split into training and test sets to support the replicability of the benchmarking of QA systems: |
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""" |
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_URL = "https://github.com/crux82/squad-it/raw/master/" |
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_URLS = { |
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"train": _URL + "SQuAD_it-train.json.gz", |
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"test": _URL + "SQuAD_it-test.json.gz", |
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} |
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class SquadIt(datasets.GeneratorBasedBuilder): |
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"""TODO(squad_it): Short description of my dataset.""" |
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VERSION = datasets.Version("0.1.0") |
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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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"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/crux82/squad-it", |
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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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"""Returns SplitGenerators.""" |
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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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] |
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def _generate_examples(self, filepath): |
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"""Yields examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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data = json.load(f) |
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for example in data["data"]: |
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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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yield id_, { |
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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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