Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Languages:
Catalan
Size:
100K - 1M
License:
File size: 5,709 Bytes
a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 fbf36cb a054402 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
# Loading script for the TeCla dataset.
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
Baucells, Irene, Carrino, Casimiro Pio, Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021).
TeCla: Text Classification Catalan dataset (Version 2.0) [Data set].
Zenodo. http://doi.org/10.5281/zenodo.7334110
"""
_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 = "./"
_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 2.0 dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label1": datasets.features.ClassLabel
(names=
[
"Societat",
"Pol\u00edtica",
"Economia",
"Cultura",
]
),
"label2": datasets.features.ClassLabel
(names=
[
"Llengua",
"Infraestructures",
"Arts",
"Parlament",
"Noves tecnologies",
"Castells",
"Successos",
"Empresa",
"Mobilitat",
"Teatre",
"Treball",
"Log\u00edstica",
"Urbanisme",
"Govern",
"Entitats",
"Finances",
"Govern espanyol",
"Tr\u00e0nsit",
"Ind\u00fastria",
"Esports",
"Exteriors",
"Medi ambient",
"Habitatge",
"Salut",
"Equipaments i patrimoni",
"Recerca",
"Cooperaci\u00f3",
"Innovaci\u00f3",
"Agroalimentaci\u00f3",
"Policial",
"Serveis Socials",
"Cinema",
"Mem\u00f2ria hist\u00f2rica",
"Turisme",
"Pol\u00edtica municipal",
"Comer\u00e7",
"Universitats",
"Hisenda",
"Judicial",
"Partits",
"M\u00fasica",
"Lletres",
"Religi\u00f3",
"Festa i cultura popular",
"Uni\u00f3 Europea",
"Moda",
"Moviments socials",
"Comptes p\u00fablics",
"Immigraci\u00f3",
"Educaci\u00f3",
"Gastronomia",
"Meteorologia",
"Energia"
]
),
}
),
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"]
label1 = article["label1"]
label2 = article["label2"]
yield id_, {
"text": text,
"label1": label1,
"label2": label2,
}
|