Datasets:
eduge

	# 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}