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# Vietnamese Legal Text BERT
#### Table of contents
1. [Introduction](#introduction)
2. [Using Vietnamese Legal Text BERT](#transformers)
- [Installation](#install2)
- [Pre-trained models](#models2)
- [Example usage](#usage2)
# <a name="introduction"></a> Using Vietnamese Legal Text BERT `hmthanh/VietnamLegalText-SBERT`
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
## <a name="transformers"></a> Using Vietnamese Legal Text BERT `transformers`
### Installation <a name="install2"></a>
- Install `transformers` with pip:
`pip install transformers`<br />
- Install `tokenizers` with pip:
`pip install tokenizers`
### Pre-trained models <a name="models2"></a>
Model | #params | Arch. | Max length | Pre-training data
---|---|---|---|---
`hmthanh/VietnamLegalText-SBERT` | 135M | base | 256 | 20GB of texts
### Example usage <a name="usage2"></a>
```python
import torch
from transformers import AutoModel, AutoTokenizer
phobert = AutoModel.from_pretrained("hmthanh/VietnamLegalText-SBERT")
tokenizer = AutoTokenizer.from_pretrained("hmthanh/VietnamLegalText-SBERT")
sentence = 'Vượt đèn đỏ bị phạt bao nhiêu tiền?'
input_ids = torch.tensor([tokenizer.encode(sentence)])
with torch.no_grad():
features = phobert(input_ids) # Models outputs are now tuples
```
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