Datasets:
mkqa

	# 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.
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	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""MKQA: Multilingual Knowledge Questions & Answers"""


	import json

	import datasets


	_CITATION = """\
	@misc{mkqa,
	title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering},
	author = {Shayne Longpre and Yi Lu and Joachim Daiber},
	year = {2020},
	URL = {https://arxiv.org/pdf/2007.15207.pdf}
	}
	"""

	_DESCRIPTION = """\
	We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs sampled from the Google Natural Questions dataset, aligned across 26 typologically diverse languages (260k question-answer pairs in total). For each query we collected new passage-independent answers. These queries and answers were then human translated into 25 Non-English languages.
	"""
	_HOMEPAGE = "https://github.com/apple/ml-mkqa"
	_LICENSE = "CC BY-SA 3.0"


	_URLS = {"train": "https://github.com/apple/ml-mkqa/raw/main/dataset/mkqa.jsonl.gz"}


	class Mkqa(datasets.GeneratorBasedBuilder):
	"""MKQA dataset"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="mkqa",
	version=VERSION,
	description=_DESCRIPTION,
	),
	]

	def _info(self):
	langs = [
	"ar",
	"da",
	"de",
	"en",
	"es",
	"fi",
	"fr",
	"he",
	"hu",
	"it",
	"ja",
	"ko",
	"km",
	"ms",
	"nl",
	"no",
	"pl",
	"pt",
	"ru",
	"sv",
	"th",
	"tr",
	"vi",
	"zh_cn",
	"zh_hk",
	"zh_tw",
	]

	# Preferring list type instead of datasets.Sequence
	queries_features = {lan: datasets.Value("string") for lan in langs}
	answer_feature = [
	{
	"type": datasets.ClassLabel(
	names=[
	"entity",
	"long_answer",
	"unanswerable",
	"date",
	"number",
	"number_with_unit",
	"short_phrase",
	"binary",
	]
	),
	"entity": datasets.Value("string"),
	"text": datasets.Value("string"),
	"aliases": [datasets.Value("string")],
	}
	]
	answer_features = {lan: answer_feature for lan in langs}

	features = datasets.Features(
	{
	"example_id": datasets.Value("string"),
	"queries": queries_features,
	"query": datasets.Value("string"),
	"answers": answer_features,
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# download and extract URLs
	urls_to_download = _URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	for row in f:
	data = json.loads(row)
	data["example_id"] = str(data["example_id"])
	id_ = data["example_id"]
	for language in data["answers"].keys():
	# Add default values for possible missing keys
	for a in data["answers"][language]:
	if "aliases" not in a:
	a["aliases"] = []
	if "entity" not in a:
	a["entity"] = ""

	yield id_, data