Update README.md

README.md

@@ -9,6 +9,10 @@ language:
 
 ## CodeSage-Large
 
+### Updates
+* [12/2024] <span style="color:blue">We are excited to announce the release of the CodeSage V2 model family with largely improved performance and flexible embedding dimensions!</span> Please check out our [models](https://huggingface.co/codesage) and [blogpost](https://code-representation-learning.github.io/codesage-v2.html) for more details.
+* [11/2024] You can now access CodeSage models through SentenceTransformer.
+
 ### Model description
 CodeSage is a new family of open code embedding models with an encoder architecture that support a wide range of source code understanding tasks. It is introduced in the paper:
 
@@ -21,25 +25,31 @@ This checkpoint is trained on the Stack data (https://huggingface.co/datasets/bi
 ### Training procedure
 This checkpoint is first trained on code data via masked language modeling (MLM) and then on bimodal text-code pair data. Please refer to the paper for more details.
 
-### How to use
-This checkpoint consists of an encoder (1.3B model), which can be used to extract code embeddings of 2048 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 (1.3B 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
 
 checkpoint = "codesage/codesage-large"
 device = "cuda"  # for GPU usage or "cpu" for CPU usage
 
-# Note: CodeSage requires adding eos token at the end of
-# each tokenized sequence to ensure good performance
+# Note: CodeSage requires adding eos token at the end of each tokenized sequence 
+
 tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True, add_eos_token=True)
 
 model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).to(device)
 
 inputs = tokenizer.encode("def print_hello_world():\tprint('Hello World!')", return_tensors="pt").to(device)
 embedding = model(inputs)[0]
-print(f'Dimension of the embedding: {embedding[0].size()}')
-# Dimension of the embedding: torch.Size([14, 2048])
+```
+
+2. Accessing CodeSage via SentenceTransformer
+```
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer("codesage/codesage-large", trust_remote_code=True)
 ```
 
 ### BibTeX entry and citation info