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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.
"""The dataset contains a set of 31,030 Arabic newspaper articles alongwith metadata, extracted from various online Saudi newspapers."""
import json
import os
import datasets
_CITATION = """\
@misc{hagrima2015,
author = "M. Alhagri",
title = "Saudi Newspapers Arabic Corpus (SaudiNewsNet)",
year = 2015,
url = "http://github.com/ParallelMazen/SaudiNewsNet"
}
"""
_DESCRIPTION = """The dataset contains a set of 31,030 Arabic newspaper articles alongwith metadata, \
extracted from various online Saudi newspapers and written in MSA."""
_HOMEPAGE = "https://github.com/parallelfold/SaudiNewsNet"
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License."
_URLs = [
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-21.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-22.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-23.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-24.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-25.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-26.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-27.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-31.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-01.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-02.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-03.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-04.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-06.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-07.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-08.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-09.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-10.zip",
"https://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-08-11.zip",
]
_dirs = [
"2015-07-21.json",
"2015-07-22.json",
"2015-07-23.json",
"2015-07-24.json",
"2015-07-25.json",
"2015-07-26.json",
"2015-07-27.json",
"2015-07-31.json",
"2015-08-01.json",
"2015-08-02.json",
"2015-08-03.json",
"2015-08-04.json",
"2015-08-06.json",
"2015-08-07.json",
"2015-08-08.json",
"2015-08-09.json",
"2015-08-10.json",
"2015-08-11.json",
]
class Saudinewsnet(datasets.GeneratorBasedBuilder):
"""a set of 31,030 Arabic newspaper articles along with metadata"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"source": datasets.Value(
"string"
), # A string identifief of the newspaper from which the article was extracted.
"url": datasets.Value("string"), # The full URL from which the article was extracted.
"date_extracted": datasets.Value(
"string"
), # The timestamp of the date on which the article was extracted.
"title": datasets.Value("string"), # The title of the article. Can be empty.
"author": datasets.Value("string"), # The author of the article. Can be empty.
"content": datasets.Value("string"), # The content of the article.
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
datadir = dl_manager.download_and_extract(_URLs)
pt = []
for dd, d in zip(datadir, _dirs):
pt.append(os.path.join(dd, d))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": pt, "split": "train"},
)
]
def _generate_examples(self, filepath, split):
"""Generates examples"""
for file_idx, path in enumerate(filepath):
with open(path, encoding="utf-8") as f:
articles = json.load(f)
for _id, article in enumerate(articles):
title = article.get("title", "")
source = article["source"]
dt = article["date_extracted"]
link = article["url"]
author = article.get("author", "").strip(" ")
content = article["content"].strip("/n")
yield f"{file_idx}_{_id}", {
"title": title,
"source": source,
"date_extracted": dt,
"url": link,
"author": author,
"content": content,
}
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