paddlepaddle--paddlenlp
wehub-resource-sync
78 行
2.8 KiB
Python
78 行
2.8 KiB
Python
# Copyright (c) 2021 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 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__ = ["NLPCC_DBQA"]
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class NLPCC_DBQA(DatasetBuilder):
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"""
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NLPCC2016 DBQA dataset.
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Document-based QA (or DBQA) task
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When predicting answers to each question, a DBQA system built by each
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participating team IS LIMITED TO select sentences as answers from the
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question’s given document.
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For more information: http://tcci.ccf.org.cn/conference/2016/dldoc/evagline2.pdf
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"""
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URL = "https://bj.bcebos.com/paddlenlp/datasets/nlpcc-dbqa.zip"
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MD5 = "a5f69c2462136ef4d1707e4e2551a57b"
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META_INFO = collections.namedtuple("META_INFO", ("file", "md5"))
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SPLITS = {
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"train": META_INFO(os.path.join("nlpcc-dbqa", "nlpcc-dbqa", "train.tsv"), "4f84fefce1a8f52c8d9248d1ff5ab9bd"),
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"dev": META_INFO(os.path.join("nlpcc-dbqa", "nlpcc-dbqa", "dev.tsv"), "3831beb0d42c29615d06343538538f53"),
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"test": META_INFO(os.path.join("nlpcc-dbqa", "nlpcc-dbqa", "test.tsv"), "e224351353b1f6a15837008b5d0da703"),
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}
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def _get_data(self, mode, **kwargs):
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"""Downloads dataset."""
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default_root = os.path.join(DATA_HOME, self.__class__.__name__)
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filename, data_hash = 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(self.URL, default_root, self.MD5)
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return fullname
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def _read(self, filename, split):
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"""Reads data."""
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with open(filename, "r", encoding="utf-8") as f:
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head = None
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for line in f:
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data = line.strip().split("\t")
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if not head:
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head = data
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else:
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qid, text_a, text_b, label = data
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yield {"qid": qid, "text_a": text_a, "text_b": text_b, "label": label}
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def get_labels(self):
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"""
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Return labels of XNLI dataset.
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Note:
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Contradictory and contradiction are the same label
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"""
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return ["0", "1"]
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