Datasets:
Tasks:
Text Classification
Sub-tasks:
topic-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
extended|other-yahoo-answers-corpus
Tags:
License:
| # 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 | |