|
|
|
"""20ng question classification dataset.""" |
|
|
|
|
|
import csv |
|
|
|
import datasets |
|
from datasets.tasks import TextClassification |
|
import sys |
|
csv.field_size_limit(sys.maxsize) |
|
|
|
_DESCRIPTION = """ |
|
""" |
|
|
|
_CITATION = """jigsaw_toxicity_pred""" |
|
|
|
_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/toxic_comments/raw/main/train.csv" |
|
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/toxic_comments/raw/main/test.csv" |
|
_VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/toxic_comments/raw/main/validation.csv" |
|
|
|
|
|
CATEGORY_MAPPING = ['neutral', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'] |
|
|
|
class NG(datasets.GeneratorBasedBuilder): |
|
"""toxic_comments classification dataset.""" |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
"text": datasets.Value("string"), |
|
"label": datasets.Sequence(datasets.ClassLabel(names=CATEGORY_MAPPING)), |
|
} |
|
), |
|
homepage="", |
|
citation=_CITATION, |
|
|
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) |
|
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL) |
|
valid_path = dl_manager.download_and_extract(_VALID_DOWNLOAD_URL) |
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), |
|
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}), |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
"""Generate examples.""" |
|
with open(filepath, encoding="utf-8") as csv_file: |
|
csv_reader = csv.reader( |
|
csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True |
|
) |
|
_ = next(csv_reader) |
|
for id_, row in enumerate(csv_reader): |
|
text, label = row |
|
label = [int(ind) for ind in label.strip(']').strip('[').split(",")] |
|
yield id_, {"text": text, "label": label} |
