Datasets:
parsinlu_reading_comprehension

	# 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.
	"""ParsiNLU Persian reading comprehension task"""


	import json

	import datasets


	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@article{huggingface:dataset,
	title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
	authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
	year={2020}
	journal = {arXiv e-prints},
	eprint = {2012.06154},
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	A Persian reading comprehenion task (generating an answer, given a question and a context paragraph).
	The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers.
	"""

	_HOMEPAGE = "https://github.com/persiannlp/parsinlu/"

	_LICENSE = "CC BY-NC-SA 4.0"

	_URL = "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/reading_comprehension/"
	_URLs = {
	"train": _URL + "train.jsonl",
	"dev": _URL + "dev.jsonl",
	"test": _URL + "eval.jsonl",
	}


	class ParsinluReadingComprehension(datasets.GeneratorBasedBuilder):
	"""ParsiNLU Persian reading comprehension task."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="parsinlu-repo", version=VERSION, description="ParsiNLU repository: reading-comprehension"
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"question": datasets.Value("string"),
	"url": datasets.Value("string"),
	"context": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"answer_start": datasets.Value("int32"),
	"answer_text": datasets.Value("string"),
	}
	),
	}
	)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_URLs)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": data_dir["test"], "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["dev"],
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	logger.info("generating examples from = %s", filepath)

	def get_answer_index(passage, answer):
	return passage.index(answer) if answer in passage else -1

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	answer = data["answers"]
	if type(answer[0]) == str:
	answer = [{"answer_start": get_answer_index(data["passage"], x), "answer_text": x} for x in answer]
	else:
	answer = [{"answer_start": x[0], "answer_text": x[1]} for x in answer]
	yield id_, {
	"question": data["question"],
	"url": str(data["url"]),
	"context": data["passage"],
	"answers": answer,
	}