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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.
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """
@inproceedings{,
author = {Nguyen, Luan Thanh and Van Nguyen, Kiet and Nguyen, Ngan Luu-Thuy},
title = {Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese},
booktitle = {Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices},
year = {2021},
publisher = {Springer International Publishing},
address = {Kuala Lumpur, Malaysia},
pages = {572--583},
}
"""
_LOCAL = False
_LANGUAGES = ["vie"]
_DATASETNAME = "uit_victsd"
_DESCRIPTION = """
The UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) is a compilation of 10,000 human-annotated
comments intended for constructive and toxic comments detection. The dataset spans 10 domains, reflecting the diverse topics
and expressions found in social media interactions among Vietnamese users.
"""
_HOMEPAGE = "https://github.com/tarudesu/ViCTSD"
_LICENSE = Licenses.UNKNOWN.value
_URL = "https://huggingface.co/datasets/tarudesu/ViCTSD"
_SUPPORTED_TASKS = [Tasks.INTENT_CLASSIFICATION, Tasks.ABUSIVE_LANGUAGE_PREDICTION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class UiTViCTSDDataset(datasets.GeneratorBasedBuilder):
"""
Dataset of Vietnamese social media comments annotated
for constructiveness and toxicity.
"""
SUBSETS = ["constructiveness", "toxicity"]
CLASS_LABELS = [0, 1]
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema for {subset} subset",
schema="source",
subset_id=f"{_DATASETNAME}_{subset}",
)
for subset in SUBSETS
] + [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_seacrowd_text",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
schema="seacrowd_text",
subset_id=f"{_DATASETNAME}_{subset}",
)
for subset in SUBSETS
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_constructiveness_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"Unnamed: 0": datasets.Value("int64"), # Column name missing in original dataset
"Comment": datasets.Value("string"),
"Constructiveness": datasets.ClassLabel(names=self.CLASS_LABELS),
"Toxicity": datasets.ClassLabel(names=self.CLASS_LABELS),
"Title": datasets.Value("string"),
"Topic": 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]:
# dl_manager not used since dataloader uses HF 'load_dataset'
return [datasets.SplitGenerator(name=split, gen_kwargs={"split": split._name}) for split in (datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST)]
def _load_hf_data_from_remote(self, split: str) -> datasets.DatasetDict:
"""Load dataset from HuggingFace."""
HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
_hf_dataset_source = datasets.load_dataset(HF_REMOTE_REF, split=split)
return _hf_dataset_source
def _generate_examples(self, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
data = self._load_hf_data_from_remote(split=split)
for index, row in enumerate(data):
if self.config.schema == "source":
example = row
elif self.config.schema == "seacrowd_text":
if "constructiveness" in self.config.name:
label = row["Constructiveness"]
elif "toxicity" in self.config.name:
label = row["Toxicity"]
example = {"id": str(index), "text": row["Comment"], "label": label}
yield index, example
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