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---
language:
- vi
thumbnail: "https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png"
tags:
- transfomer
- sbert
- legaltext
- vietnamese
license: "mit"
datasets:
- vietnamese-legal-text
---
# 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`
## <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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