main_cyberbully_splitted.py

# 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}