Datasets:
arabic_billion_words

	# 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.
	"""Arabic Billion Words Corpus"""


	import os
	import re

	import datasets


	_CITATION = """\
	@article{el20161,
	title={1.5 billion words arabic corpus},
	author={El-Khair, Ibrahim Abu},
	journal={arXiv preprint arXiv:1611.04033},
	year={2016}
	}
	"""

	_DESCRIPTION = """\
	Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
	It contains over a billion and a half words in total, out of which, there are about three million unique words.
	The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
	Also it was marked with two mark-up languages, namely: SGML, and XML.
	"""

	_HOMEPAGE = "http://abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus"

	_URL = "http://abuelkhair.net/corpus/"
	_URLs = {
	"Alittihad": _URL + "Alittihad_XML_utf_8.rar",
	"Almasryalyoum": _URL + "Almasryalyoum_XML_utf_8.rar",
	"Almustaqbal": _URL + "Almustaqbal_XML_utf_8.rar",
	"Alqabas": _URL + "Alqabas_XML_utf_8.rar",
	"Echoroukonline": _URL + "Echoroukonline_XML_utf_8.rar",
	"Ryiadh": _URL + "Ryiadh_XML_utf_8.rar",
	"Sabanews": _URL + "Sabanews_XML_utf_8.rar",
	"SaudiYoum": _URL + "SaudiYoum_XML_utf_8.rar",
	"Techreen": _URL + "Techreen_XML_utf_8.rar",
	"Youm7": _URL + "Youm7_XML_utf_8.rar",
	}

	# Some tags are misspelled
	# - Misspelled article tags:
	# - Alqabas: <Alqabas>, <Alqabas1>
	# - Ryiadh: <Ryiadh>, <Ryiadh1>
	MISSPELLED_TAGS = {
	"Dateline": ["Dateline", "dateline"],
	"Headline": ["Headline", "Healine"],
	"Text": ["Text"],
	"URL": ["URL"],
	}

	TAG_PATTERNS = {
	tag: [re.compile(rf".?<{label}>(.?)</{label}>.*?", re.MULTILINE \| re.DOTALL) for label in labels]
	for tag, labels in MISSPELLED_TAGS.items()
	}


	class ArabicBillionWords(datasets.GeneratorBasedBuilder):
	"""Arabic Billion Words Corpus"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="Alittihad", version=VERSION, description="This part of dataset covers Alittihad news paper"
	),
	datasets.BuilderConfig(
	name="Almasryalyoum", version=VERSION, description="This part of dataset covers Almasryalyoum news paper"
	),
	datasets.BuilderConfig(
	name="Almustaqbal", version=VERSION, description="This part of dataset covers Almustaqbal news paper"
	),
	datasets.BuilderConfig(
	name="Alqabas", version=VERSION, description="This part of dataset covers Alqabas news paper"
	),
	datasets.BuilderConfig(
	name="Echoroukonline", version=VERSION, description="This part of dataset covers Echoroukonline news paper"
	),
	datasets.BuilderConfig(
	name="Ryiadh", version=VERSION, description="This part of dataset covers Ryiadh news paper"
	),
	datasets.BuilderConfig(
	name="Sabanews", version=VERSION, description="This part of dataset covers Sabanews news paper"
	),
	datasets.BuilderConfig(
	name="SaudiYoum", version=VERSION, description="This part of dataset covers SaudiYoum news paper"
	),
	datasets.BuilderConfig(
	name="Techreen", version=VERSION, description="This part of dataset covers Techreen news paper"
	),
	datasets.BuilderConfig(
	name="Youm7", version=VERSION, description="This part of dataset covers Youm7 news paper"
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"url": datasets.Value("string"),
	"head_line": datasets.Value("string"),
	"date": datasets.Value("string"),
	"text": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	my_urls = _URLs[self.config.name]
	data_dir = dl_manager.download_and_extract(my_urls)
	my_file_name = f"{self.config.name}_utf_8.xml"
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, my_file_name),
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	data_tag = self.config.name
	pattern = re.compile(rf".?<{data_tag}(.?)</{data_tag}.*?", re.MULTILINE \| re.DOTALL)
	key = 0
	lines = ""
	with open(filepath, mode="r", encoding="utf-8") as f:
	for i, line in enumerate(f):
	lines += line
	if f"</{data_tag}" in line:
	match = pattern.match(lines)
	lines = ""
	if match:
	record = match.group(1)
	text = self._clean_text(self._extract_tag("Text", record))
	url = self._extract_tag("URL", record)
	head_line = self._clean_text(self._extract_tag("Headline", record))
	date = self._extract_tag("Dateline", record)
	yield key, {"url": url, "head_line": head_line, "date": date, "text": text}
	key += 1

	@staticmethod
	def _extract_tag(tag, text):
	# check if the tag is misspelled
	for pattern in TAG_PATTERNS[tag]:
	match = pattern.match(text)
	if match:
	return match.group(1)
	return ""

	@staticmethod
	def _clean_text(text):
	return text.replace("?", "")