Update README.md

README.md

@@ -58,9 +58,11 @@ For this V2 model, we enhanced semantic search performance by improving the qual
 ### Training Data
 This pretrained checkpoint is the same as those used by our V1 model ([codesage/codesage-small](https://huggingface.co/codesage/codesage-small), which is trained on [The Stack](https://huggingface.co/datasets/bigcode/the-stack-dedup) data. The constative learning data are extracted from [The Stack V2](https://huggingface.co/datasets/bigcode/the-stack-v2). Same as our V1 model, we supported nine languages as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.
 
-### How to use
-This checkpoint consists of an encoder (130M model), which can be used to extract code embeddings of 1024 dimension. It can be easily loaded using the AutoModel functionality and employs the [Starcoder Tokenizer](https://arxiv.org/pdf/2305.06161.pdf).
+### How to Use
+This checkpoint consists of an encoder (356M model), which can be used to extract code embeddings of 1024 dimension. 
 
+1. Accessing CodeSage via HuggingFace: it can be easily loaded using the AutoModel functionality and employs the [Starcoder Tokenizer](https://arxiv.org/pdf/2305.06161.pdf).
+   
 ```
 from transformers import AutoModel, AutoTokenizer
 
@@ -77,6 +79,12 @@ inputs = tokenizer.encode("def print_hello_world():\tprint('Hello World!')", ret
 embedding = model(inputs)[0]
 ```
 
+2. Accessing CodeSage via SentenceTransformer
+```
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer("codesage/codesage-base-v2", trust_remote_code=True)
+```
+
 ### BibTeX entry and citation info
 ```
 @inproceedings{