readme: ---
  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
  ```
  