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Delete loading script
Browse files- sogou_news.py +0 -94
sogou_news.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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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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# Lint as: python3
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"""Sogou News"""
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import csv
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import ctypes
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import datasets
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csv.field_size_limit(int(ctypes.c_ulong(-1).value // 2))
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_CITATION = """\
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@misc{zhang2015characterlevel,
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title={Character-level Convolutional Networks for Text Classification},
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author={Xiang Zhang and Junbo Zhao and Yann LeCun},
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year={2015},
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eprint={1509.01626},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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"""
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_DESCRIPTION = """\
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The Sogou News dataset is a mixture of 2,909,551 news articles from the SogouCA and SogouCS news corpora, in 5 categories.
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The number of training samples selected for each class is 90,000 and testing 12,000. Note that the Chinese characters have been converted to Pinyin.
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classification labels of the news are determined by their domain names in the URL. For example, the news with
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URL http://sports.sohu.com is categorized as a sport class.
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"""
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_DATA_URL = "https://s3.amazonaws.com/fast-ai-nlp/sogou_news_csv.tgz"
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class Sogou_News(datasets.GeneratorBasedBuilder):
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"""Sogou News dataset"""
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"title": datasets.Value("string"),
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"content": datasets.Value("string"),
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"label": datasets.features.ClassLabel(
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names=["sports", "finance", "entertainment", "automobile", "technology"]
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),
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}
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),
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# No default supervised_keys (as we have to pass both premise
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# and hypothesis as input).
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supervised_keys=None,
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homepage="", # didn't find a real 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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archive = dl_manager.download(_DATA_URL)
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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={"filepath": "sogou_news_csv/test.csv", "files": dl_manager.iter_archive(archive)},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"filepath": "sogou_news_csv/train.csv", "files": dl_manager.iter_archive(archive)},
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),
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]
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def _generate_examples(self, filepath, files):
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"""This function returns the examples in the raw (text) form."""
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for path, f in files:
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if path == filepath:
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lines = (line.decode("utf-8") for line in f)
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data = csv.reader(lines)
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for id_, row in enumerate(data):
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yield id_, {"title": row[1], "content": row[2], "label": int(row[0]) - 1}
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break
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