Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Languages:
English
Size:
n<1K
Tags:
License:
File size: 2,984 Bytes
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# 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,
}
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