|
|
|
|
|
--- |
|
|
license: cc-by-nc-4.0 |
|
|
language: |
|
|
- vie |
|
|
pretty_name: Visobert |
|
|
task_categories: |
|
|
- self-supervised-pretraining |
|
|
tags: |
|
|
- self-supervised-pretraining |
|
|
--- |
|
|
|
|
|
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. |
|
|
|
|
|
|
|
|
## Languages |
|
|
|
|
|
vie |
|
|
|
|
|
## Supported Tasks |
|
|
|
|
|
Self Supervised Pretraining |
|
|
|
|
|
## Dataset Usage |
|
|
### Using `datasets` library |
|
|
``` |
|
|
from datasets import load_dataset |
|
|
dset = datasets.load_dataset("SEACrowd/visobert", trust_remote_code=True) |
|
|
``` |
|
|
### Using `seacrowd` library |
|
|
```import seacrowd as sc |
|
|
# Load the dataset using the default config |
|
|
dset = sc.load_dataset("visobert", schema="seacrowd") |
|
|
# Check all available subsets (config names) of the dataset |
|
|
print(sc.available_config_names("visobert")) |
|
|
# Load the dataset using a specific config |
|
|
dset = sc.load_dataset_by_config_name(config_name="<config_name>") |
|
|
``` |
|
|
|
|
|
More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use). |
|
|
|
|
|
|
|
|
## Dataset Homepage |
|
|
|
|
|
[https://huggingface.co/uitnlp/visobert](https://huggingface.co/uitnlp/visobert) |
|
|
|
|
|
## Dataset Version |
|
|
|
|
|
Source: 1.0.0. SEACrowd: 2024.06.20. |
|
|
|
|
|
## Dataset License |
|
|
|
|
|
Creative Commons Attribution Non Commercial 4.0 (cc-by-nc-4.0) |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you are using the **Visobert** dataloader in your work, please cite the following: |
|
|
``` |
|
|
@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.", |
|
|
} |
|
|
|
|
|
|
|
|
@article{lovenia2024seacrowd, |
|
|
title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, |
|
|
author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, |
|
|
year={2024}, |
|
|
eprint={2406.10118}, |
|
|
journal={arXiv preprint arXiv: 2406.10118} |
|
|
} |
|
|
|
|
|
``` |
