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# Lint as: python3
"""Cyberbullying classification dataset."""
import csv
import datasets
from datasets.tasks import TextClassification
import sys
csv.field_size_limit(sys.maxsize)
_DESCRIPTION = """\
This is a dataset for cyberbullying in bangla.
"""
_CITATION = """"""
_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/samanjoy2/main_cyberbully_splitted/resolve/main/train.csv"
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/samanjoy2/main_cyberbully_splitted/resolve/main/test.csv"
_VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/samanjoy2/main_cyberbully_splitted/resolve/main/validaton.csv"
CATEGORY_MAPPING = {'not bully': 0,
'sexual': 1,
'religious': 2,
'threat': 3,
'troll': 4
}
class NG(datasets.GeneratorBasedBuilder):
"""20ng classification dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=list(CATEGORY_MAPPING.keys())),
}
),
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 = CATEGORY_MAPPING[label]
yield id_, {"text": text, "label": label} |