Datasets:
journalists_questions

	# 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.


	import csv

	import datasets


	_CITATION = """\
	@inproceedings{hasanain2016questions,
	title={What Questions Do Journalists Ask on Twitter?},
	author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W},
	booktitle={Tenth International AAAI Conference on Web and Social Media},
	year={2016}
	}
	"""

	_DESCRIPTION = """\
	The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic
	tweets manually labeled for question identification over Arabic tweets posted by journalists.
	"""
	_DATA_URL = "https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U"


	class JournalistsQuestions(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="plain_text",
	version=datasets.Version("1.0.0", ""),
	description="Journalists tweet IDs and annotation by whether the tweet has a question",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"tweet_id": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["no", "yes"]),
	"label_confidence": datasets.Value("float"),
	}
	),
	homepage="http://qufaculty.qu.edu.qa/telsayed/datasets/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	dl_dir = dl_manager.download_and_extract(_DATA_URL)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_dir}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f, delimiter="\t", fieldnames=["tweet_id", "label", "label_confidence"])
	for idx, row in enumerate(reader):
	yield idx, {
	"tweet_id": row["tweet_id"],
	"label": row["label"],
	"label_confidence": float(row["label_confidence"]),
	}