Datasets:
hard

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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
	"""Hotel Reviews in Arabic language"""


	import os

	import datasets
	from datasets.tasks import TextClassification


	_DESCRIPTION = """\
	This dataset contains 93700 hotel reviews in Arabic language.\
	The hotel reviews were collected from Booking.com website during June/July 2016.\
	The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.\
	The following table summarize some tatistics on the HARD Dataset.
	"""

	_CITATION = """\
	@incollection{elnagar2018hotel,
	title={Hotel Arabic-reviews dataset construction for sentiment analysis applications},
	author={Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas},
	booktitle={Intelligent Natural Language Processing: Trends and Applications},
	pages={35--52},
	year={2018},
	publisher={Springer}
	}
	"""

	_DOWNLOAD_URL = "https://raw.githubusercontent.com/elnagara/HARD-Arabic-Dataset/master/data/balanced-reviews.zip"


	class HardConfig(datasets.BuilderConfig):
	"""BuilderConfig for Hard."""

	def __init__(self, **kwargs):
	"""BuilderConfig for Hard.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(HardConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	class Hard(datasets.GeneratorBasedBuilder):
	"""Hard dataset."""

	BUILDER_CONFIGS = [
	HardConfig(
	name="plain_text",
	description="Plain text",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"1",
	"2",
	"3",
	"4",
	"5",
	]
	),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/elnagara/HARD-Arabic-Dataset",
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "balanced-reviews.txt")}
	),
	]

	def _generate_examples(self, directory):
	"""Generate examples."""
	with open(directory, mode="r", encoding="utf-16") as file:
	for id_, line in enumerate(file.read().splitlines()[1:]):
	_, _, rating, _, _, _, review_text = line.split("\t")
	yield str(id_), {"text": review_text, "label": rating}