Datasets:
onestop_qa

	# 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.
	"""OneStopQA - a multiple choice reading comprehension dataset annotated
	according to the STARC (Structured Annotations for Reading Comprehension) scheme"""


	import json
	import os

	import datasets


	# from datasets.tasks import QuestionAnsweringExtractive


	logger = datasets.logging.get_logger(__name__)


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{starc2020,
	author = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger},
	title = {STARC: Structured Annotations for Reading Comprehension},
	booktitle = {ACL},
	year = {2020},
	publisher = {Association for Computational Linguistics}
	}
	"""

	_DESCRIPTION = """\
	OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC \
	(Structured Annotations for Reading Comprehension) scheme. \
	The reading materials are Guardian articles taken from the \
	[OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). \
	Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. \
	Each paragraph is annotated with three multiple choice reading comprehension questions. \
	The reading comprehension questions can be answered based on any of the three paragraph levels.
	"""

	_HOMEPAGE = "https://github.com/berzak/onestop-qa"

	_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License"

	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URL = "https://github.com/berzak/onestop-qa/raw/master/annotations/onestop_qa.zip"


	class OneStopQA(datasets.GeneratorBasedBuilder):
	"""OneStopQA - a multiple choice reading comprehension dataset annotated
	according to the STARC (Structured Annotations for Reading Comprehension) scheme"""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')

	def _info(self):
	features = datasets.Features(
	{
	"title": datasets.Value("string"),
	"paragraph": datasets.Value("string"),
	"level": datasets.ClassLabel(names=["Adv", "Int", "Ele"]),
	"question": datasets.Value("string"),
	"paragraph_index": datasets.Value("int32"),
	"answers": datasets.features.Sequence(datasets.Value("string"), length=4),
	"a_span": datasets.features.Sequence(datasets.Value("int32")),
	"d_span": datasets.features.Sequence(datasets.Value("int32")),
	}
	)

	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,
	task_templates=[]
	# QuestionAnsweringExtractive(
	# question_column="question", context_column="context", answers_column="answers"
	# )
	# ], # When issue #2434 is resolved uncomment task_templates and the QuestionAnsweringExtractive (or similar)
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	data_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "onestop_qa.json"),
	"split": "train",
	},
	),
	]

	def _generate_examples(
	self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	"""Yields examples as (key, example) tuples."""
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.
	# Based on the squad dataset
	logger.info("generating examples from = %s", filepath)
	key = 0
	with open(filepath, encoding="utf-8") as f:
	onestop_qa = json.load(f)
	for article in onestop_qa["data"]:
	title = article.get("title", "")
	for paragraph_index, paragraph in enumerate(article["paragraphs"]):
	for level in ["Adv", "Int", "Ele"]:
	paragraph_context_and_spans = paragraph[level]
	paragraph_context = paragraph_context_and_spans["context"]
	a_spans = paragraph_context_and_spans["a_spans"]
	d_spans = paragraph_context_and_spans["d_spans"]
	qas = paragraph["qas"]
	for qa, a_span, d_span in zip(qas, a_spans, d_spans):
	yield key, {
	"title": title,
	"paragraph": paragraph_context,
	"question": qa["question"],
	"paragraph_index": paragraph_index,
	"answers": qa["answers"],
	"level": level,
	"a_span": a_span,
	"d_span": d_span,
	},

	key += 1