Datasets:
xor_tydi_qa

	"""XOR QA: Cross-lingual Open-Retrieval Question Answering"""


	import json
	import textwrap

	import datasets


	_XOR_TYDI_QA_CITATION = """\
	@misc{asai2020xor,
	title={XOR QA: Cross-lingual Open-Retrieval Question Answering},
	author={Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi},
	year={2020},
	eprint={2010.11856},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_XOR_TYDI_QA_DESCRIPTION = """\
	XOR-TyDi QA brings together for the first time information-seeking questions,
	open-retrieval QA, and multilingual QA to create a multilingual open-retrieval
	QA dataset that enables cross-lingual answer retrieval. It consists of questions
	written by information-seeking native speakers in 7 typologically diverse languages
	and answer annotations that are retrieved from multilingual document collections.
	There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.
	"""

	_DESCRIPTIONS = {
	"xor-retrieve": textwrap.dedent(
	"""\
	XOR-Retrieve is a cross-lingual retrieval task where a question is written in the target
	language (e.g., Japanese) and a system is required to retrieve English document that answers the question.
	"""
	),
	"xor-full": textwrap.dedent(
	"""\
	XOR-Full is a cross-lingual retrieval task where a question is written in the target
	language (e.g., Japanese) and a system is required to output a short answer in the target language."""
	),
	}

	_DATA_URLS = {
	"xor-retrieve": {
	"train": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_train_retrieve_eng_span.jsonl",
	"dev": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_retrieve_eng_span_v1_1.jsonl",
	"test": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_test_retrieve_eng_span_q_only_v1_1.jsonl",
	},
	"xor-full": {
	"train": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_train_full.jsonl",
	"dev": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_full_v1_1.jsonl",
	"test": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_test_full_q_only_v1_1.jsonl",
	},
	}

	_XOR_TYDI_QA_URL = "https://nlp.cs.washington.edu/xorqa/"


	class XORTyDiConfig(datasets.BuilderConfig):
	"BuilderConfig for XOR-TyDi Dataset"

	def __init__(self, data_url, citation, url, **kwargs):
	"""
	Args:

	data_url: `dictionary`, dict with url for each split of data.
	citation: `string`, citation for the dataset.
	url: `string`, url for information about the dataset.
	**kwargs: keyword arguments forwarded to super.
	"""
	super(XORTyDiConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
	self.data_url = data_url
	self.citation = citation
	self.url = url


	class XORTyDi(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	XORTyDiConfig(
	name=name,
	description=_DESCRIPTIONS[name],
	data_url=_DATA_URLS[name],
	citation=_XOR_TYDI_QA_CITATION,
	url=_XOR_TYDI_QA_URL,
	)
	for name in ["xor-retrieve", "xor-full"]
	]

	def _info(self):
	features = {}
	features["question"] = datasets.Value("string")
	features["lang"] = datasets.features.ClassLabel(names=["ar", "bn", "fi", "ja", "ko", "ru", "te"])
	features["answers"] = datasets.Value("string")

	return datasets.DatasetInfo(
	description=_XOR_TYDI_QA_DESCRIPTION + "\n" + self.config.description,
	features=datasets.Features(features),
	homepage=self.config.url,
	citation=_XOR_TYDI_QA_CITATION,
	)

	def _split_generators(self, dl_manager):
	train = dl_manager.download_and_extract(self.config.data_url["train"])
	dev = dl_manager.download_and_extract(self.config.data_url["dev"])
	test = dl_manager.download_and_extract(self.config.data_url["test"])

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
	]

	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf-8") as f:
	jlines = f.read()
	result = [json.loads(jline) for jline in jlines.splitlines()]
	if split == "test":
	for id_, row in enumerate(result):
	yield id_, {"question": row["question"], "answers": "None", "lang": row["lang"].strip()}
	else:
	for id_, row in enumerate(result):
	yield id_, {
	"question": row["question"],
	"answers": " ".join(row["answers"]),
	"lang": row["lang"].strip(),
	}