wehub-resource-sync
98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import json
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import os
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from paddle.dataset.common import md5file
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from paddle.utils.download import get_path_from_url
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from ..utils.env import DATA_HOME
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from .dataset import DatasetBuilder
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__all__ = ["SQuAD"]
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class SQuAD(DatasetBuilder):
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"""
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Stanford Question Answering Dataset (SQuAD) is a reading comprehension
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dataset, consisting of questions posed by crowdworkers on a set of Wikipedia
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articles, where the answer to every question is a segment of text, or span,
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from the corresponding reading passage, or the question might be unanswerable.
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"""
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META_INFO = collections.namedtuple("META_INFO", ("file", "md5", "URL"))
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SPLITS = {
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"train_v1": META_INFO(
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os.path.join("train-v1.1.json"),
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"981b29407e0affa3b1b156f72073b945",
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"https://bj.bcebos.com/paddlenlp/datasets/squad/train-v1.1.json",
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),
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"dev_v1": META_INFO(
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os.path.join("dev-v1.1.json"),
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"3e85deb501d4e538b6bc56f786231552",
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"https://bj.bcebos.com/paddlenlp/datasets/squad/dev-v1.1.json",
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),
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"train_v2": META_INFO(
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os.path.join("train-v2.0.json"),
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"62108c273c268d70893182d5cf8df740",
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"https://bj.bcebos.com/paddlenlp/datasets/squad/train-v2.0.json",
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),
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"dev_v2": META_INFO(
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os.path.join("dev-v2.0.json"),
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"246adae8b7002f8679c027697b0b7cf8",
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"https://bj.bcebos.com/paddlenlp/datasets/squad/dev-v2.0.json",
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),
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}
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def _get_data(self, mode, **kwargs):
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default_root = os.path.join(DATA_HOME, self.__class__.__name__)
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filename, data_hash, URL = self.SPLITS[mode]
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fullname = os.path.join(default_root, filename)
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if not os.path.exists(fullname) or (data_hash and not md5file(fullname) == data_hash):
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get_path_from_url(URL, default_root)
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return fullname
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def _read(self, filename, *args):
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with open(filename, "r", encoding="utf8") as f:
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input_data = json.load(f)["data"]
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for entry in input_data:
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title = entry.get("title", "").strip()
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for paragraph in entry["paragraphs"]:
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context = paragraph["context"].strip()
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for qa in paragraph["qas"]:
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qas_id = qa["id"]
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question = qa["question"].strip()
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answer_starts = []
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answers = []
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is_impossible = False
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if "is_impossible" in qa.keys():
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is_impossible = qa["is_impossible"]
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answer_starts = [answer["answer_start"] for answer in qa.get("answers", [])]
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answers = [answer["text"].strip() for answer in qa.get("answers", [])]
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yield {
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"id": qas_id,
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"title": title,
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"context": context,
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"question": question,
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"answers": answers,
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"answer_starts": answer_starts,
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"is_impossible": is_impossible,
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}
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