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# Loading script for the IntoxiCat dataset.
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """ """
_DESCRIPTION = """ InToxiCat is a dataset for the detection of abusive language in Catalan. """
_HOMEPAGE = """ https://huggingface.co/datasets/projecte-aina/InToxiCat"""
_URL = "https://huggingface.co/datasets/projecte-aina/InToxicat/resolve/main/"
_FILE_TRAIN = "train.json"
_FILE_DEV = "dev.json"
_FILE_TEST = "test.json"
class InToxiCatConfig(datasets.BuilderConfig):
""" Builder config for the InToxiCat dataset """
def __init__(self, **kwargs):
"""BuilderConfig for InToxiCat.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(InToxiCatConfig, self).__init__(**kwargs)
class InToxiCat(datasets.GeneratorBasedBuilder):
""" InToxiCat Dataset """
BUILDER_CONFIGS = [
InToxiCatConfig(
name="intoxicat",
version=datasets.Version("1.0.0"),
description="InToxiCat dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"context": datasets.Value("string"),
"sentence": datasets.Value("string"),
"topic": datasets.Value("string"),
"keywords": datasets.Sequence(datasets.Value("string")),
"context_needed": datasets.Value("string"),
"is_abusive": datasets.features.ClassLabel(names=['abusive','not_abusive']),
"abusiveness_agreement": datasets.Value("string"),
"target_type": datasets.Sequence(datasets.features.ClassLabel(names=['INDIVIDUAL','GROUP','OTHERS'])),
"abusive_spans": datasets.Sequence(feature={'text': datasets.Value(dtype='string', id=None), 'index': datasets.Value(dtype='string', id=None)}, length=-1, id=None), #datasets.Sequence(feature=datasets.Sequence(datasets.Value(dtype='string', id=None))),
"target_spans": datasets.Sequence(feature={'text': datasets.Value(dtype='string', id=None), 'index': datasets.Value(dtype='string', id=None)}, length=-1, id=None),
"is_implicit": datasets.Value("string")
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_FILE_TRAIN}",
"dev": f"{_FILE_DEV}",
"test": f"{_FILE_TEST}"
}
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)
data = json.load(open(filepath, 'r'))
for id_, example in enumerate(data):
yield id_, {
"id": example["id"],
"context": example["context"],
"sentence": example["sentence"],
"topic": example["topic"],
"keywords": example["key_words"],
"context_needed": example["annotation"]["context_needed"] if example["annotation"]["context_needed"] else None,
"is_abusive": example["annotation"]["is_abusive"] if example["annotation"]["is_abusive"] else None,
"abusiveness_agreement": example["annotation"]["abusiveness_agreement"],
"target_type": example["annotation"]["target_type"] if example["annotation"]["target_type"] else None,
"abusive_spans": {
"text": [text for text, _ in example["annotation"]["abusive_spans"]],
"index": [index for _, index in example["annotation"]["abusive_spans"]]
} if example["annotation"]["abusive_spans"] != [] else None,
"target_spans": {
"text": [text for text, _ in example["annotation"]["target_spans"]],
"index": [index for _, index in example["annotation"]["target_spans"]]
} if example["annotation"]["target_spans"] != [] else None,
"is_implicit": example["annotation"]["is_implicit"] if example["annotation"]["is_implicit"] != "" else None
} |