Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
automatically-generated
Tags:
License:
| # Loading script for the WikiCAT dataset. | |
| import json | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """ | |
| """ | |
| _DESCRIPTION = """ | |
| WikiCAT: Text Classification English dataset from the Viquipedia | |
| """ | |
| _HOMEPAGE = """ """ | |
| # TODO: upload datasets to github | |
| _URL = "https://huggingface.co/datasets/crodri/wikicat_en/resolve/main/" | |
| _TRAINING_FILE = "hftrain_en.json" | |
| _DEV_FILE = "hfeval_en.json" | |
| #_TEST_FILE = "test.json" | |
| class wikicat_enConfig(datasets.BuilderConfig): | |
| """ Builder config for the Topicat dataset """ | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for wikicat_en. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(teclaConfig, self).__init__(**kwargs) | |
| class wikicat_en(datasets.GeneratorBasedBuilder): | |
| """ wikicat_en Dataset """ | |
| BUILDER_CONFIGS = [ | |
| wikicat_enConfig( | |
| name="wikicat_en", | |
| version=datasets.Version("1.1.0"), | |
| description="wikicat_en", | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "text": datasets.Value("string"), | |
| "label": datasets.features.ClassLabel | |
| (names= ['Health', 'Law', 'Entertainment', 'Religion', 'Business', 'Science', 'Engineering', 'Nature', 'Philosophy', 'Economy', 'Sports', 'Technology', 'Government', 'Mathematics', 'Military', 'Humanities', 'Music', 'Politics', 'History'] | |
| ), | |
| } | |
| ), | |
| 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: | |
| wikicat_en = json.load(f) | |
| for id_, article in enumerate(wikicat_en["data"]): | |
| text = article["sentence"] | |
| label = article["label"] | |
| yield id_, { | |
| "text": text, | |
| "label": label, | |
| } | |