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--- |
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pipeline_tag: fill-mask |
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widget: |
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- text: hào quang rực <mask> |
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language: |
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- vi |
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tags: |
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- Vietnamese |
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- Social Media |
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- Vietnamese Pre-trained Model |
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- Sentiment Analysis |
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- Hate Speech Detection |
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- Spam Detection |
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- Emotionn Recognition |
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--- |
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# <a name="introduction"></a> ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing (EMNLP 2023 - Main) |
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**Disclaimer**: The paper contains actual comments on social networks that might be construed as abusive, offensive, or obscene. |
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ViSoBERT is the state-of-the-art language model for Vietnamese social media tasks: |
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- ViSoBERT is the first monolingual MLM ([XLM-R](https://github.com/facebookresearch/XLM#xlm-r-new-model) architecture) built specifically for Vietnamese social media texts. |
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- ViSoBERT outperforms previous monolingual, multilingual, and multilingual social media approaches, obtaining new state-of-the-art performances on four downstream Vietnamese social media tasks. |
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The general architecture and experimental results of ViSoBERT can be found in our [paper](https://aclanthology.org/2023.emnlp-main.315/): |
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@inproceedings{nguyen-etal-2023-visobert, |
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title = "{V}i{S}o{BERT}: A Pre-Trained Language Model for {V}ietnamese Social Media Text Processing", |
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author = "Nguyen, Nam and |
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Phan, Thang and |
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Nguyen, Duc-Vu and |
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Nguyen, Kiet", |
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editor = "Bouamor, Houda and |
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Pino, Juan and |
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Bali, Kalika", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.emnlp-main.315", |
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pages = "5191--5207", |
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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.", |
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} |
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The pretraining dataset of our paper is available at: [Pretraining dataset](https://drive.google.com/drive/folders/1C144LOlkbH78m0-JoMckpRXubV7XT7Kb) |
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**Please CITE** our paper when ViSoBERT is used to help produce published results or is incorporated into other software. |
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**Installation** |
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Install `transformers` and `SentencePiece` packages: |
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pip install transformers |
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pip install SentencePiece |
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**Example usage** |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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import torch |
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model= AutoModel.from_pretrained('uitnlp/visobert') |
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tokenizer = AutoTokenizer.from_pretrained('uitnlp/visobert') |
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encoding = tokenizer('hào quang rực rỡ', return_tensors='pt') |
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with torch.no_grad(): |
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output = model(**encoding) |
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``` |
