Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
Mongolian
Size:
10K<n<100K
License:
File size: 3,358 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Eduge news topic classification dataset."""
import csv
import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """\
Eduge news classification dataset is provided by Bolorsoft LLC. It is used for training the Eduge.mn production news classifier
75K news articles in 9 categories: урлаг соёл, эдийн засаг, эрүүл мэнд, хууль, улс төр, спорт, технологи, боловсрол and байгал орчин
"""
_TRAIN_DOWNLOAD_URL = "https://storage.googleapis.com/eduge_dataset/eduge_train.csv"
_TEST_DOWNLOAD_URL = "https://storage.googleapis.com/eduge_dataset/eduge_test.csv"
class Eduge(datasets.GeneratorBasedBuilder):
"""Eduge news topic classification dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"news": datasets.Value("string"),
"label": datasets.features.ClassLabel(
names=[
"урлаг соёл",
"эдийн засаг",
"эрүүл мэнд",
"хууль",
"улс төр",
"спорт",
"технологи",
"боловсрол",
"байгал орчин",
]
),
}
),
homepage="http://eduge.mn",
task_templates=[
TextClassification(
text_column="news",
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 Eduge 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
)
next(csv_reader)
for id_, row in enumerate(csv_reader):
news, label = row[0], row[1]
yield id_, {"news": news, "label": label}
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