105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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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 datasets
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import os
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import pandas as pd
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_CITATION = """\
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@inproceedings{lai-etal-2017-race,
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title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations",
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author = "Lai, Guokun and
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Xie, Qizhe and
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Liu, Hanxiao and
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Yang, Yiming and
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Hovy, Eduard",
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booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
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month = sep,
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year = "2017",
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address = "Copenhagen, Denmark",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/D17-1082",
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doi = "10.18653/v1/D17-1082",
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pages = "785--794",
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}
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"""
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_DESCRIPTION = """\
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RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions.
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"""
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_HOMEPAGE = 'https://modelscope.cn/datasets/modelscope/race/summary'
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_URL = 'https://modelscope.cn/api/v1/datasets/modelscope/race/repo?Revision=master&FilePath=race.zip'
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task_list = [
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'high',
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'middle',
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]
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class RACEConfig(datasets.BuilderConfig):
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def __init__(self, **kwargs):
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super().__init__(version=datasets.Version('1.0.0'), **kwargs)
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class RACE(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [RACEConfig(name=task_name, ) for task_name in task_list]
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def _info(self):
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features = datasets.Features({
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'example_id': datasets.Value('string'),
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'article': datasets.Value('string'),
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'answer': datasets.Value('string'),
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'question': datasets.Value('string'),
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'options': [datasets.Value('string')],
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})
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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data_dir = dl_manager.download_and_extract(_URL)
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task_name = self.config.name
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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'filepath': os.path.join(data_dir, f'race/test/{task_name}-00000-of-00001.parquet'),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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'filepath': os.path.join(data_dir, f'race/val/{task_name}-00000-of-00001.parquet'),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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'filepath': os.path.join(data_dir, f'race/train/{task_name}-00000-of-00001.parquet'),
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},
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),
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]
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def _generate_examples(self, filepath):
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df = pd.read_parquet(filepath)
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df.columns = ['example_id', 'article', 'answer', 'question', 'options']
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for i, instance in enumerate(df.to_dict(orient='records')):
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yield i, instance
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