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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Dict, List, Tuple
import datasets
import pandas
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """
@InProceedings{10.1007/978-3-030-79457-6_35,
author="Luu, Son T.
and Nguyen, Kiet Van
and Nguyen, Ngan Luu-Thuy",
editor="Fujita, Hamido
and Selamat, Ali
and Lin, Jerry Chun-Wei
and Ali, Moonis",
title="A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts",
booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="415--426",
abstract="In recent years, Vietnam witnesses the mass development of social network users on different social
platforms such as Facebook, Youtube, Instagram, and Tiktok. On social media, hate speech has become a critical
problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for
automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment
in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process
for annotating and evaluating the quality of the dataset. Finally, we evaluate the dataset by deep learning and transformer models.",
isbn="978-3-030-79457-6"
}
"""
_LOCAL = False
_LANGUAGES = ["vie"]
_DATASETNAME = "uit_vihsd"
_DESCRIPTION = """
The ViHSD dataset consists of comments collected from Facebook pages and YouTube channels that have a
high-interactive rate, and do not restrict comments. This dataset is used for hate speech detection on
Vietnamese language. Data is anonymized, and labeled as either HATE, OFFENSIVE, or CLEAN.
"""
_HOMEPAGE = "https://github.com/sonlam1102/vihsd/"
_LICENSE = Licenses.UNKNOWN.value
_URL = "https://raw.githubusercontent.com/sonlam1102/vihsd/main/data/vihsd.zip"
_Split_Path = {
"train": "vihsd/train.csv",
"validation": "vihsd/dev.csv",
"test": "vihsd/test.csv",
}
_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class UiTVihsdDataset(datasets.GeneratorBasedBuilder):
"""
The SeaCrowd dataloader for the dataset Vietnamese Hate Speech Detection (UIT-ViHSD).
"""
CLASS_LABELS = ["CLEAN", "OFFENSIVE", "HATE"] # 0:CLEAN, 1:OFFENSIVE, 2:HATE
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_text",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema ",
schema="seacrowd_text",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("int64"),
"text": datasets.Value("string"),
"label": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_text":
features = schemas.text_features(label_names=self.CLASS_LABELS)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
file_paths = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["train"])},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["validation"])},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["test"])},
),
]
def _generate_examples(self, filepath) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
data_lines = pandas.read_csv(filepath)
for row in data_lines.itertuples():
if self.config.schema == "source":
example = {"id": str(row.Index), "text": row.free_text, "label": row.label_id}
if self.config.schema == "seacrowd_text":
example = {"id": str(row.Index), "text": row.free_text, "label": self.CLASS_LABELS[int(row.label_id)]}
yield row.Index, example
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