Datasets:
xquad_r

	# 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.
	"""TODO: Add a description here."""


	import json

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{roy2020lareqa,
	title={LAReQA: Language-agnostic answer retrieval from a multilingual pool},
	author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei},
	journal={arXiv preprint arXiv:2004.05484},
	year={2020}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://github.com/google-research-datasets/lareqa"

	# TODO: Add link to the official dataset URLs here
	# 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/google-research-datasets/lareqa/raw/master/xquad-r/"
	_LANG = ["ar", "de", "zh", "vi", "en", "es", "hi", "el", "th", "tr", "ru"]


	class XquadRConfig(datasets.BuilderConfig):

	"""BuilderConfig for XquadR"""

	def __init__(self, lang, **kwargs):
	"""
	Args:
	lang: string, language for the input text
	**kwargs: keyword arguments forwarded to super.
	"""
	super(XquadRConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.lang = lang


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class XquadR(datasets.GeneratorBasedBuilder):
	"""TODO(xquad-r): Short description of my dataset."""

	# TODO(xquad-r): Set up version.
	VERSION = datasets.Version("1.1.0")
	BUILDER_CONFIGS = [XquadRConfig(name=f"{lang}", description=_DESCRIPTION, lang=lang) for lang in _LANG]

	def _info(self):
	# TODO(xquad-r): Specifies the datasets.DatasetInfo object
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	}
	),
	}
	),
	# 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,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(xquad-r): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	urls_to_download = {lang: _URL + f"{lang}.json" for lang in _LANG}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": downloaded_files[self.config.lang]},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(xquad-r): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for article in data["data"]:
	for paragraph in article["paragraphs"]:
	context = paragraph["context"].strip()
	for qa in paragraph["qas"]:
	question = qa["question"].strip()
	id_ = qa["id"]

	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"].strip() for answer in qa["answers"]]

	# Features currently used are "context", "question", and "answers".
	# Others are extracted here for the ease of future expansions.
	yield id_, {
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}