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"""TODO(cmrc2018): 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{cui-emnlp2019-cmrc2018, |
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title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension}, |
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author = {Cui, Yiming and |
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Liu, Ting and |
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Che, Wanxiang and |
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Xiao, Li and |
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Chen, Zhipeng and |
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Ma, Wentao and |
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Wang, Shijin and |
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Hu, Guoping}, |
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booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, |
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month = {nov}, |
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year = {2019}, |
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address = {Hong Kong, China}, |
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publisher = {Association for Computational Linguistics}, |
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url = {https://www.aclweb.org/anthology/D19-1600}, |
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doi = {10.18653/v1/D19-1600}, |
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pages = {5886--5891}} |
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""" |
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_DESCRIPTION = """\ |
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A Span-Extraction dataset for Chinese machine reading comprehension to add language |
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diversities in this area. The dataset is composed by near 20,000 real questions annotated |
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on Wikipedia paragraphs by human experts. We also annotated a challenge set which |
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contains the questions that need comprehensive understanding and multi-sentence |
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inference throughout the context. |
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""" |
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_URL = "https://github.com/ymcui/cmrc2018" |
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_TRAIN_FILE = "https://worksheets.codalab.org/rest/bundles/0x15022f0c4d3944a599ab27256686b9ac/contents/blob/" |
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_DEV_FILE = "https://worksheets.codalab.org/rest/bundles/0x72252619f67b4346a85e122049c3eabd/contents/blob/" |
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_TEST_FILE = "https://worksheets.codalab.org/rest/bundles/0x182c2e71fac94fc2a45cc1a3376879f7/contents/blob/" |
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class Cmrc2018(datasets.GeneratorBasedBuilder): |
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"""TODO(cmrc2018): 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=_URL, |
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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": _TRAIN_FILE, "dev": _DEV_FILE, "test": _TEST_FILE} |
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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["dev"]}), |
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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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