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| # coding=utf-8 | |
| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | |
| # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # Lint as: python3 | |
| import json | |
| import os | |
| import datasets | |
| from datasets.tasks import QuestionAnsweringExtractive | |
| logger = datasets.logging.get_logger(__name__) | |
| _DESCRIPTION = """\ | |
| DureaderRobust is a chinese reading comprehension \ | |
| dataset, designed to evaluate the MRC models from \ | |
| three aspects: over-sensitivity, over-stability \ | |
| and generalization. | |
| """ | |
| _URL = "https://bj.bcebos.com/paddlenlp/datasets/dureader_robust-data.tar.gz" | |
| class DureaderRobustConfig(datasets.BuilderConfig): | |
| """BuilderConfig for DureaderRobust.""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for DureaderRobust. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(DureaderRobustConfig, self).__init__(**kwargs) | |
| class DureaderRobust(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| DureaderRobustConfig( | |
| name="plain_text", | |
| version=datasets.Version("1.0.0", ""), | |
| description="Plain text", | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "context": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "answers": datasets.features.Sequence( | |
| { | |
| "text": datasets.Value("string"), | |
| "answer_start": datasets.Value("int32"), | |
| } | |
| ), | |
| } | |
| ), | |
| # No default supervised_keys (as we have to pass both question | |
| # and context as input). | |
| supervised_keys=None, | |
| homepage="https://arxiv.org/abs/2004.11142", | |
| task_templates=[ | |
| QuestionAnsweringExtractive( | |
| question_column="question", context_column="context", answers_column="answers" | |
| ) | |
| ], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| dl_dir = dl_manager.download_and_extract(_URL) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir,'dureader_robust-data', 'train.json')}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dl_dir,'dureader_robust-data', 'dev.json')}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir,'dureader_robust-data', 'test.json')}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| logger.info("generating examples from = %s", filepath) | |
| key = 0 | |
| with open(filepath, encoding="utf-8") as f: | |
| durobust = json.load(f) | |
| for article in durobust["data"]: | |
| title = article.get("title", "") | |
| for paragraph in article["paragraphs"]: | |
| context = paragraph["context"] # do not strip leading blank spaces GH-2585 | |
| for qa in paragraph["qas"]: | |
| answer_starts = [answer["answer_start"] for answer in qa.get("answers",'')] | |
| answers = [answer["text"] for answer in qa.get("answers",'')] | |
| # Features currently used are "context", "question", and "answers". | |
| # Others are extracted here for the ease of future expansions. | |
| yield key, { | |
| "title": title, | |
| "context": context, | |
| "question": qa["question"], | |
| "id": qa["id"], | |
| "answers": { | |
| "answer_start": answer_starts, | |
| "text": answers, | |
| }, | |
| } | |
| key += 1 | |