Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
topic-classification
Languages:
German
Size:
10K - 100K
License:
File size: 3,452 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.
"""Ten Thousand German News Articles Dataset"""
import csv
import datasets
_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/",
)
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}
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