metadata

pipeline_tag: fill-mask
widget:
  - text: hào quang rực <mask>

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing (EMNLP 2023 - Main)

Disclaimer: The paper contains actual comments on social networks that might be construed as abusive, offensive, or obscene.

ViSoBERT is the state-of-the-art language model for Vietnamese social media tasks:

ViSoBERT is the first monolingual MLM (XLM-R architecture) built specifically for Vietnamese social media texts.
ViSoBERT outperforms previous monolingual, multilingual, and multilingual social media approaches, obtaining new state-of-the-art performances on four downstream Vietnamese social media tasks.

The general architecture and experimental results of ViSoBERT can be found in our paper:

@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.",
}

The pretraining dataset of our paper is available at: Pretraining dataset

Please CITE our paper when ViSoBERT is used to help produce published results or is incorporated into other software.

Installation

Install transformers and SentencePiece packages:

pip install transformers
pip install SentencePiece

Example usage

from transformers import AutoModel, AutoTokenizer
import torch

model= AutoModel.from_pretrained('uitnlp/visobert')
tokenizer = AutoTokenizer.from_pretrained('uitnlp/visobert')

encoding = tokenizer('hào quang rực rỡ', return_tensors='pt')

with torch.no_grad():
  output = model(**encoding)