Datasets:

PlanTL-GOB-ES
/

SQAC

	# Loading script for the SQAC dataset.
	import json
	import datasets

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """
	bibtex
	@article{DBLP:journals/corr/abs-2107-07253,
	author = {Asier Guti{\'{e}}rrez{-}Fandi{\~{n}}o and
	Jordi Armengol{-}Estap{\'{e}} and
	Marc P{\`{a}}mies and
	Joan Llop{-}Palao and
	Joaqu{\'{\i}}n Silveira{-}Ocampo and
	Casimiro Pio Carrino and
	Aitor Gonzalez{-}Agirre and
	Carme Armentano{-}Oller and
	Carlos Rodr{\'{\i}}guez Penagos and
	Marta Villegas},
	title = {Spanish Language Models},
	journal = {CoRR},
	volume = {abs/2107.07253},
	year = {2021},
	url = {https://arxiv.org/abs/2107.07253},
	archivePrefix = {arXiv},
	eprint = {2107.07253},
	timestamp = {Wed, 21 Jul 2021 15:55:35 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-2107-07253.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DESCRIPTION = """
	This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.

	The sources of the contexts are:

	* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).

	* News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).

	* Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence] (https://creativecommons.org/licenses/by/4.0/legalcode).

	This dataset can be used to build extractive-QA.
	"""

	_HOMEPAGE = """"""

	_URL = "https://huggingface.co/datasets/BSC-TeMU/SQAC/resolve/main/"
	_TRAINING_FILE = "train.json"
	_DEV_FILE = "dev.json"
	_TEST_FILE = "test.json"


	class SQACConfig(datasets.BuilderConfig):
	""" Builder config for the SQAC dataset """

	def __init__(self, **kwargs):
	"""BuilderConfig for SQAC.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(SQACConfig, self).__init__(**kwargs)


	class SQAC(datasets.GeneratorBasedBuilder):
	"""SQAC Dataset."""

	BUILDER_CONFIGS = [
	SQACConfig(
	name="SQAC",
	#version=datasets.Version("1.0.1"),
	description="SQAC dataset",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	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"),
	}
	),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	"dev": f"{_URL}{_DEV_FILE}",
	"test": f"{_URL}{_TEST_FILE}",
	}
	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):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	sqac_data = json.load(f)
	for article in sqac_data["data"]:
	title = article.get("title", "").strip()
	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_, {
	"title": title,
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}