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# 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 pathlib import Path
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{nguyen-etal-2023-visobert,
title = "{V}i{S}o{BERT}: A Pre-Trained Language Model for {V}ietnamese Social Media Text Processing",
author = "Nguyen, Nam and
Phan, Thang and
Nguyen, Duc-Vu and
Nguyen, Kiet",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.315",
pages = "5191--5207",
abstract = "English and Chinese, known as resource-rich languages, have witnessed the strong
development of transformer-based language models for natural language processing tasks. Although
Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT,
ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and
named entity recognition. These pre-trained language models are still limited to Vietnamese social
media tasks. In this paper, we present the first monolingual pre-trained language model for
Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality
and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our
pre-trained model on five important natural language downstream tasks on Vietnamese social media
texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and
hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters,
surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our
ViSoBERT model is available only for research purposes. Disclaimer: This paper contains actual
comments on social networks that might be construed as abusive, offensive, or obscene.",
}
"""
_DATASETNAME = "visobert"
_DESCRIPTION = """\
The ViSoBERT corpus is composed of Vietnamese textual data crawled from Facebook, TikTok, and YouTube. The
dataset contains Facebook posts, TikTok comments, and Youtube comments of Vietnamese-verified users, from
Jan 2016 (Jan 2020 for TikTok) to Dec 2022. A post-processing mechanism is applied to handles hashtags,
emojis, misspellings, hyperlinks, and other noncanonical texts.
"""
_HOMEPAGE = "https://huggingface.co/uitnlp/visobert"
_LANGUAGES = ["vie"]
_LICENSE = Licenses.CC_BY_NC_4_0.value
_LOCAL = False
_URLS = "https://drive.usercontent.google.com/download?id=1BoiR9k2DrjBcd2aHy5BOq4haEp5V2_ug&confirm=xxx"
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class ViSoBERTDataset(datasets.GeneratorBasedBuilder):
"""
The ViSoBERT corpus is a Vietnamese pretraining dataset from https://huggingface.co/uitnlp/visobert.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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_ssp",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema",
schema="seacrowd_ssp",
subset_id=f"{_DATASETNAME}",
),
]
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source" or self.config.schema == "seacrowd_ssp":
features = schemas.self_supervised_pretraining.features
else:
raise ValueError(f"Invalid schema: '{self.config.schema}'")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""
Returns SplitGenerators.
"""
path = dl_manager.download(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": path,
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""
Yields examples as (key, example) tuples.
"""
with open(filepath, "r", encoding="utf-8") as f:
if self.config.schema == "source" or self.config.schema == "seacrowd_ssp":
for idx, row in enumerate(f):
if row.strip() != "":
yield (
idx,
{
"id": str(idx),
"text": row.strip(),
},
)
else:
raise ValueError(f"Invalid config: '{self.config.name}'") |