Delete loading script

sogou_news.py

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