Datasets:

projecte-aina
/

teca

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

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """
	@inproceedings{armengol-estape-etal-2021-multilingual,
	title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
	author = "Armengol-Estap{\'e}, Jordi and
	Carrino, Casimiro Pio and
	Rodriguez-Penagos, Carlos and
	de Gibert Bonet, Ona and
	Armentano-Oller, Carme and
	Gonzalez-Agirre, Aitor and
	Melero, Maite and
	Villegas, Marta",
	booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
	month = aug,
	year = "2021",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2021.findings-acl.437",
	doi = "10.18653/v1/2021.findings-acl.437",
	pages = "4933--4946",
	}
	"""

	_DESCRIPTION = """
	TECA consists of two subsets of textual entailment in Catalan, catalan_TE1 and vilaweb_TE, which contain 14997 and 6166 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction or neutral). This dataset was developed by BSC TeMU as part of the AINA project and intended as part of the Catalan Language Understanding Benchmark (CLUB).
	"""

	_HOMEPAGE = """https://zenodo.org/record/4621378"""

	# TODO: upload datasets to github
	_URL = "https://huggingface.co/datasets/projecte-aina/teca/resolve/main/"
	_TRAINING_FILE = "train.json"
	_DEV_FILE = "dev.json"
	_TEST_FILE = "test.json"


	class tecaConfig(datasets.BuilderConfig):
	""" Builder config for the TECA dataset """

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


	class teca(datasets.GeneratorBasedBuilder):
	""" TECA Dataset """

	BUILDER_CONFIGS = [
	tecaConfig(
	name="teca",
	version=datasets.Version("1.0.1"),
	description="teca dataset",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"premise": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.features.ClassLabel
	(names=
	[
	"entailment",
	"neutral",
	"contradiction"
	]
	),
	}
	),
	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:
	data_dict = json.load(f)
	for id_, article in enumerate(data_dict["data"]):
	original_id = article["id"]
	premise = article["premise"]
	hypothesis = article["hypothesis"]
	label = article["label"]
	yield id_, {
	"id": original_id,
	"premise": premise,
	"hypothesis": hypothesis,
	"label": label,
	}