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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.
# Lint as: python3
"""Turkish News Category Dataset (270K) - Interpress Media Monitoring Company"""
import csv
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
It is a Turkish news data set consisting of 273601 news in 17 categories, compiled from print media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company.
"""
_CITATION = ""
_LICENSE = "unknown"
_HOMEPAGE = "https://www.interpress.com/"
_DOWNLOAD_URL = "https://www.interpress.com/downloads/interpress_news_category_tr_270k.zip"
_DATASET_URLS = {
"train": "interpress_news_category_tr_270k_train.tsv",
"test": "interpress_news_category_tr_270k_test.tsv",
}
class InterpressNewsCategoryTRConfig(datasets.BuilderConfig):
"""BuilderConfig for InterpressNewsCategoryTR."""
def __init__(self, **kwargs):
"""BuilderConfig for InterpressNewsCategoryTR.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(InterpressNewsCategoryTRConfig, self).__init__(**kwargs)
class InterpressNewsCategoryTR(datasets.GeneratorBasedBuilder):
"""Turkish News Category Dataset (270K) - Interpress Media Monitoring Company"""
BUILDER_CONFIGS = [
InterpressNewsCategoryTRConfig(
name="270k",
version=datasets.Version("1.0.0"),
description="Turkish News Category Dataset (270K) - Interpress Media Monitoring Company",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"title": datasets.Value("string"),
"content": datasets.Value("string"),
"category": datasets.features.ClassLabel(
names=[
"aktuel",
"bilisim",
"egitim",
"ekonomi",
"gida",
"iletisim",
"kultursanat",
"magazin",
"saglik",
"savunma",
"seyahat",
"siyasi",
"spor",
"teknoloji",
"ticaret",
"turizm",
"yasam",
]
),
"categorycode": datasets.features.ClassLabel(num_classes=17),
"publishdatetime": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir, _DATASET_URLS["train"])}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir, _DATASET_URLS["test"])}
),
]
def _generate_examples(self, filepath):
"""Generate InterpressNewsCategoryTR examples."""
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for idx, row in enumerate(reader):
yield idx, {
"id": row["ID"],
"title": row["Title"],
"content": row["Content"],
"category": row["Category"],
"categorycode": row["CategoryCode"],
"publishdatetime": row["PublishDateTime"],
}
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