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# Lint as: python3
"""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,
# task_templates=[TextClassification(text_column="text", label_column="label")],
)
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) # skip header
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} |