Datasets:

vesteinn
/

icelandic-qa-NQiI

	# Adapted from the SQuAD dataset file

	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	_CITATION = """\
	"""

	_DESCRIPTION = """\
	"""

	_URL = "https://repository.clarin.is/repository/xmlui/bitstream/handle/20.500.12537/143/"
	_URLS = {
	"train": _URL + "nqii_train_squad_format_tok.json",
	"dev": _URL + "nqii_dev_squad_format_tok.json",
	"test": _URL + "nqii_test_squad_format_tok.json"
	}


	class NQiIConfig(datasets.BuilderConfig):
	"""BuilderConfig."""

	def __init__(self, **kwargs):
	"""BuilderConfig.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(NQiIConfig, self).__init__(**kwargs)


	class NQiI(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	NQiIConfig(name="icelandic-qa-NQiI", version=datasets.Version("1.0.0"), description=""),
	]

	def _info(self):
	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"),
	"title": datasets.Value("string"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	}
	),
	# 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://vesteinn.is/qa/",
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(squad_v2): 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 = _URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

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

	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(squad_v2): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	squad = json.load(f)
	for example in squad["data"]:
	title = example.get("title", "")
	for paragraph in example["paragraphs"]:
	context = paragraph["context"] # do not strip leading blank spaces GH-2585
	for qa in paragraph["qas"]:
	question = qa["question"]
	id_ = qa["id"]

	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"] 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_, {
	"title": title,
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}