Delete loading script

yahoo_answers_topics.py

@@ -1,105 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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.
-"""Yahoo! Answers Topic Classification Dataset"""
-
-
-import csv
-
-import datasets
-
-
-_DESCRIPTION = """
-Yahoo! Answers Topic Classification is text classification dataset. \
-The dataset is the Yahoo! Answers corpus as of 10/25/2007. \
-The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. \
-From all the answers and other meta-information, this dataset only used the best answer content and the main category information.
-"""
-
-_URL = "https://s3.amazonaws.com/fast-ai-nlp/yahoo_answers_csv.tgz"
-
-_TOPICS = [
-    "Society & Culture",
-    "Science & Mathematics",
-    "Health",
-    "Education & Reference",
-    "Computers & Internet",
-    "Sports",
-    "Business & Finance",
-    "Entertainment & Music",
-    "Family & Relationships",
-    "Politics & Government",
-]
-
-
-class YahooAnswersTopics(datasets.GeneratorBasedBuilder):
-    "Yahoo! Answers Topic Classification Dataset"
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="yahoo_answers_topics",
-            version=datasets.Version("1.0.0", ""),
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "topic": datasets.features.ClassLabel(names=_TOPICS),
-                    "question_title": datasets.Value("string"),
-                    "question_content": datasets.Value("string"),
-                    "best_answer": datasets.Value("string"),
-                },
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset",
-        )
-
-    def _split_generators(self, dl_manager):
-        archive = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": "yahoo_answers_csv/train.csv",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": "yahoo_answers_csv/test.csv",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        for path, f in files:
-            if path == filepath:
-                lines = (line.decode("utf-8") for line in f)
-                rows = csv.reader(lines)
-                for i, row in enumerate(rows):
-                    yield i, {
-                        "id": i,
-                        "topic": int(row[0]) - 1,
-                        "question_title": row[1],
-                        "question_content": row[2],
-                        "best_answer": row[3],
-                    }
-                break