Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
topic-classification
Languages:
English
Size:
1M - 10M
License:
File size: 3,598 Bytes
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# 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
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