File size: 3,119 Bytes
d9e372b 0c53a93 d9e372b 0c53a93 d9e372b 0c53a93 d9e372b 0c53a93 d9e372b 0c53a93 d9e372b 0c53a93 d9e372b 0c53a93 d9e372b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
# 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"""
_TRAIN_DOWNLOAD_URL = "https://drive.google.com/file/d/1Ehv1SSZ4n7ZLpUp7aSKNwHuC8UOgdfzL/view?usp=sharing"
_TEST_DOWNLOAD_URL = "https://drive.google.com/file/d/1UWUuTEkK20Pz-H0rt78n91hHeVUhtCh1/view?usp=sharing"
class AGNews(datasets.GeneratorBasedBuilder):
"""AG News topic classification dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["World", "Sports", "Business", "Sci/Tech"]),
}
),
homepage="http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html",
citation=_CITATION,
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate AG News examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
)
for id_, row in enumerate(csv_reader):
label, title, description = row
# Original labels are [1, 2, 3, 4] ->
# ['World', 'Sports', 'Business', 'Sci/Tech']
# Re-map to [0, 1, 2, 3].
label = int(label) - 1
text = " ".join((title, description))
yield id_, {"text": text, "label": label}
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
rows = csv.reader(f)
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],
} |