Update README.md

README.md

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 license: mit
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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 = 'Chúng_tôi là những nghiên_cứu_viên .'  
+
+input_ids = torch.tensor([tokenizer.encode(sentence)])
+
+with torch.no_grad():
+    features = phobert(input_ids)  # Models outputs are now tuples
+```