Delete tecla.py

tecla.py

@@ -1,157 +0,0 @@
-# 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,
-                }