Datasets:
gnad10

	# coding=utf-8
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	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
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	# http://www.apache.org/licenses/LICENSE-2.0
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	"""Ten Thousand German News Articles Dataset"""


	import csv

	import datasets
	from datasets.tasks import TextClassification


	_DESCRIPTION = """\
	This dataset is intended to advance topic classification for German texts. A classifier that is efffective in
	English may not be effective in German dataset because it has a higher inflection and longer compound words.
	The 10kGNAD dataset contains 10273 German news articles from an Austrian online newspaper categorized into
	9 categories. Article titles and text are concatenated together and authors are removed to avoid a keyword-like
	classification on authors that write frequently about one category. This dataset can be used as a benchmark
	for German topic classification.
	"""

	_HOMEPAGE = "https://tblock.github.io/10kGNAD/"

	_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0"

	_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/tblock/10kGNAD/master/train.csv"
	_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/tblock/10kGNAD/master/test.csv"


	class Gnad10(datasets.GeneratorBasedBuilder):
	"""10k German news articles for topic classification"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"Web",
	"Panorama",
	"International",
	"Wirtschaft",
	"Sport",
	"Inland",
	"Etat",
	"Wissenschaft",
	"Kultur",
	]
	),
	}
	),
	homepage="https://tblock.github.io/10kGNAD/",
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	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 German news articles examples."""

	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(csv_file, delimiter=";", quotechar="'", quoting=csv.QUOTE_ALL)
	for id_, row in enumerate(csv_reader):
	label, text = row
	yield id_, {"text": text, "label": label}