Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
Catalan
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
expert-generated
License:
File size: 3,973 Bytes
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# Loading script for the TeCla dataset.
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
Carrino, Casimiro Pio, Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021).
TeCla: Text Classification Catalan dataset (Version 1.0) [Data set].
Zenodo. http://doi.org/10.5281/zenodo.4627198
"""
_DESCRIPTION = """
TeCla: Text Classification Catalan dataset
Catalan News corpus for Text classification, crawled from ACN (Catalan News Agency) site: www.acn.cat
Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies - www.acn.cat
"""
_HOMEPAGE = """https://zenodo.org/record/4761505"""
# TODO: upload datasets to github
_URL = "https://huggingface.co/datasets/bsc/tecla/resolve/main/"
_TRAINING_FILE = "train.json"
_DEV_FILE = "dev.json"
_TEST_FILE = "test.json"
class teclaConfig(datasets.BuilderConfig):
""" Builder config for the TeCla dataset """
def __init__(self, **kwargs):
"""BuilderConfig for TeCla.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(teclaConfig, self).__init__(**kwargs)
class tecla(datasets.GeneratorBasedBuilder):
""" TeCla Dataset """
BUILDER_CONFIGS = [
teclaConfig(
name="tecla",
version=datasets.Version("1.0.1"),
description="tecla dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel
(names=
[
"Medi ambient",
"Societat",
"Policial",
"Judicial",
"Empresa",
"Partits",
"Pol\u00edtica",
"Successos",
"Salut",
"Infraestructures",
"Parlament",
"M\u00fasica",
"Govern",
"Uni\u00f3 Europea",
"Economia",
"Mobilitat",
"Treball",
"Cultura",
"Educaci\u00f3"
]
),
}
),
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:
acn_ca = json.load(f)
for id_, article in enumerate(acn_ca["data"]):
text = article["sentence"]
label = article["label"]
yield id_, {
"text": text,
"label": label,
}
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