Datasets:
wiki_qa_ar

	# 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
	"""Arabic Wiki Question Answering corpus."""


	import csv

	import datasets


	_CITATION = """\
	@InProceedings{YangYihMeek:EMNLP2015:WikiQA,
	author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
	title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
	journal = {Association for Computational Linguistics},
	year = 2015,
	doi = {10.18653/v1/D15-1237},
	pages = {2013–2018},
	}
	"""

	_DESCRIPTION = """\
	Arabic Version of WikiQA by automatic automatic machine translators \
	and crowdsourced the selection of the best one to be incorporated into the corpus
	"""

	_URL = "https://raw.githubusercontent.com/qcri/WikiQAar/master/"
	_URL_FILES = {
	"train": _URL + "WikiQAar-train.tsv",
	"dev": _URL + "WikiQAar-dev.tsv",
	"test": _URL + "WikiQAar-test.tsv",
	}


	class WikiQaArConfig(datasets.BuilderConfig):
	"""BuilderConfig for WikiQaAr."""

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

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


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

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

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"question_id": datasets.Value("string"),
	"question": datasets.Value("string"),
	"document_id": datasets.Value("string"),
	"answer_id": datasets.Value("string"),
	"answer": datasets.Value("string"),
	"label": datasets.features.ClassLabel(num_classes=2),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/qcri/WikiQAar",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	dl_dir = dl_manager.download_and_extract(_URL_FILES)

	return [
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": dl_dir["test"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_dir["dev"]}),
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_dir["train"]}),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""

	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
	for _id, row in enumerate(reader):

	# ignore some entries with errors
	if len(row) > 6 or len(row["Label"]) == 0:
	continue

	yield str(_id), {
	"question_id": row["QuestionID"],
	"question": row["Question"],
	"document_id": row["DocumentID"],
	"answer_id": row["SentenceID"],
	"answer": row["Sentence"],
	"label": row["Label"],
	}