Datasets:

facebook
/

mlqa

	"""TODO(mlqa): Add a description here."""


	import json
	import os

	import datasets


	# TODO(mlqa): BibTeX citation
	_CITATION = """\
	@article{lewis2019mlqa,
	title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
	author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
	journal={arXiv preprint arXiv:1910.07475},
	year={2019}
	}
	"""

	# TODO(mlqa):
	_DESCRIPTION = """\
	MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
	MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
	German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
	4 different languages on average.
	"""
	_URL = "https://dl.fbaipublicfiles.com/MLQA/"
	_DEV_TEST_URL = "MLQA_V1.zip"
	_TRANSLATE_TEST_URL = "mlqa-translate-test.tar.gz"
	_TRANSLATE_TRAIN_URL = "mlqa-translate-train.tar.gz"
	_LANG = ["ar", "de", "vi", "zh", "en", "es", "hi"]
	_TRANSLATE_LANG = ["ar", "de", "vi", "zh", "es", "hi"]


	class MlqaConfig(datasets.BuilderConfig):
	def __init__(self, data_url, **kwargs):
	"""BuilderConfig for MLQA

	Args:
	data_url: `string`, url to the dataset
	**kwargs: keyword arguments forwarded to super.
	"""
	super(MlqaConfig, self).__init__(
	version=datasets.Version(
	"1.0.0",
	),
	**kwargs,
	)
	self.data_url = data_url


	class Mlqa(datasets.GeneratorBasedBuilder):
	"""TODO(mlqa): Short description of my dataset."""

	# TODO(mlqa): Set up version.
	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = (
	[
	MlqaConfig(
	name="mlqa-translate-train." + lang,
	data_url=_URL + _TRANSLATE_TRAIN_URL,
	description="Machine-translated data for Translate-train (SQuAD Train and Dev sets machine-translated into "
	"Arabic, German, Hindi, Vietnamese, Simplified Chinese and Spanish)",
	)
	for lang in _LANG
	if lang != "en"
	]
	+ [
	MlqaConfig(
	name="mlqa-translate-test." + lang,
	data_url=_URL + _TRANSLATE_TEST_URL,
	description="Machine-translated data for Translate-Test (MLQA-test set machine-translated into English) ",
	)
	for lang in _LANG
	if lang != "en"
	]
	+ [
	MlqaConfig(
	name="mlqa." + lang1 + "." + lang2,
	data_url=_URL + _DEV_TEST_URL,
	description="development and test splits",
	)
	for lang1 in _LANG
	for lang2 in _LANG
	]
	)

	def _info(self):
	# TODO(mlqa): 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(
	{
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{"answer_start": datasets.Value("int32"), "text": datasets.Value("string")}
	),
	"id": datasets.Value("string"),
	# These are the features of your dataset like images, labels ...
	}
	),
	# 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="https://github.com/facebookresearch/MLQA",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(mlqa): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	if self.config.name.startswith("mlqa-translate-train"):
	archive = dl_manager.download(self.config.data_url)
	lang = self.config.name.split(".")[-1]
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": f"mlqa-translate-train/{lang}_squad-translate-train-train-v1.1.json",
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": f"mlqa-translate-train/{lang}_squad-translate-train-dev-v1.1.json",
	"files": dl_manager.iter_archive(archive),
	},
	),
	]

	else:
	if self.config.name.startswith("mlqa."):
	dl_file = dl_manager.download_and_extract(self.config.data_url)
	name = self.config.name.split(".")
	l1, l2 = name[1:]
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(
	os.path.join(dl_file, "MLQA_V1/test"),
	f"test-context-{l1}-question-{l2}.json",
	)
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(
	os.path.join(dl_file, "MLQA_V1/dev"), f"dev-context-{l1}-question-{l2}.json"
	)
	},
	),
	]
	else:
	if self.config.name.startswith("mlqa-translate-test"):
	archive = dl_manager.download(self.config.data_url)
	lang = self.config.name.split(".")[-1]
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": f"mlqa-translate-test/translate-test-context-{lang}-question-{lang}.json",
	"files": dl_manager.iter_archive(archive),
	},
	),
	]

	def _generate_examples(self, filepath, files=None):
	"""Yields examples."""
	if self.config.name.startswith("mlqa-translate"):
	for path, f in files:
	if path == filepath:
	data = json.loads(f.read().decode("utf-8"))
	break
	else:
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for examples in data["data"]:
	for example in examples["paragraphs"]:
	context = example["context"]
	for qa in example["qas"]:
	question = qa["question"]
	id_ = qa["id"]
	answers = qa["answers"]
	answers_start = [answer["answer_start"] for answer in answers]
	answers_text = [answer["text"] for answer in answers]
	yield id_, {
	"context": context,
	"question": question,
	"answers": {"answer_start": answers_start, "text": answers_text},
	"id": id_,
	}