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"""TODO(xquad): Add a description here.""" |
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import json |
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import os |
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import datasets |
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from datasets.tasks import QuestionAnsweringExtractive |
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_CITATION = """\ |
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@article{Artetxe:etal:2019, |
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author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama}, |
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title = {On the cross-lingual transferability of monolingual representations}, |
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journal = {CoRR}, |
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volume = {abs/1910.11856}, |
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year = {2019}, |
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archivePrefix = {arXiv}, |
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eprint = {1910.11856} |
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} |
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""" |
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_DESCRIPTION = """\ |
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XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering |
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performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set |
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of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, |
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Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel |
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across 12 languages. |
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""" |
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_URL = "https://raw.githubusercontent.com/summer1030/xquad-split/main/" |
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_LANG = ["th"] |
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_TRAIN_FILE = "train.json" |
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_VALID_FILE = "val.json" |
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_TEST_FILE = "test.json" |
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class XquadConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Xquad""" |
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def __init__(self, **kwargs): |
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""" |
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Args: |
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lang: string, language for the input text |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(XquadConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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class Xquad(datasets.GeneratorBasedBuilder): |
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"""TODO(xquad): Short description of my dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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XquadConfig( |
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name="xquad.th.split", |
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description=_DESCRIPTION, |
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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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"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/deepmind/xquad", |
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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 = {'TRAIN': _URL + _TRAIN_FILE, |
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'VALID':_URL + _VALID_FILE, |
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'TEST':_URL + _TEST_FILE, |
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} |
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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( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": downloaded_files['TRAIN']}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": downloaded_files['VALID']}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": downloaded_files['TEST']}, |
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), |
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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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xquad = json.load(f) |
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id_ = 0 |
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for article in xquad["data"]: |
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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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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": 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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id_ += 1 |