Datasets:

inparallel
/

saudinewsnet

	# 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,
	}