Update README.md

README.md

@@ -27,7 +27,8 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+from pyvi.ViTokenizer import tokenize
+sentences = [tokenize("This is an example sentence"), tokenize("Each sentence is converted")]
 
 model = SentenceTransformer('NghiemAbe/Vi-Legal-Bi-Encoder-v2')
 embeddings = model.encode(sentences)
@@ -52,7 +53,7 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = [tokenize("This is an example sentence"), tokenize("Each sentence is converted")]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('NghiemAbe/Vi-Legal-Bi-Encoder-v2')