# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # 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. import collections import json import os from paddle.dataset.common import md5file from paddle.utils.download import get_path_from_url from ..utils.env import DATA_HOME from .dataset import DatasetBuilder __all__ = ["DuReaderRobust"] class DuReaderRobust(DatasetBuilder): """ The machine reading comprehension dataset (i.e. DuReader robust) is designed to measure the robustness of a reading comprehension model, including the over-sensitivity, over-stability and generalization ability of the model. """ URL = "https://bj.bcebos.com/paddlenlp/datasets/dureader_robust-data.tar.gz" MD5 = "82f3d191a115ec17808856866787606e" META_INFO = collections.namedtuple("META_INFO", ("file", "md5")) SPLITS = { "train": META_INFO(os.path.join("dureader_robust-data", "train.json"), "800a3dcb742f9fdf9b11e0a83433d4be"), "dev": META_INFO(os.path.join("dureader_robust-data", "dev.json"), "ae73cec081eaa28a735204c4898a2222"), "test": META_INFO(os.path.join("dureader_robust-data", "test.json"), "e0e8aa5c7b6d11b6fc3935e29fc7746f"), } def _get_data(self, mode, **kwargs): default_root = os.path.join(DATA_HOME, self.__class__.__name__) filename, data_hash = self.SPLITS[mode] fullname = os.path.join(default_root, filename) if not os.path.exists(fullname) or (data_hash and not md5file(fullname) == data_hash): get_path_from_url(self.URL, default_root, self.MD5) return fullname def _read(self, filename, *args): with open(filename, "r", encoding="utf8") as f: input_data = json.load(f)["data"] for entry in input_data: title = entry.get("title", "").strip() for paragraph in entry["paragraphs"]: context = paragraph["context"].strip() for qa in paragraph["qas"]: qas_id = qa["id"] question = qa["question"].strip() answer_starts = [answer["answer_start"] for answer in qa.get("answers", [])] answers = [answer["text"].strip() for answer in qa.get("answers", [])] yield { "id": qas_id, "title": title, "context": context, "question": question, "answers": answers, "answer_starts": answer_starts, }