Update README.md

README.md

@@ -20,6 +20,8 @@ base_model:
 | Voyage-Code-002        | Unknown   | 68.5     | 56.3     | 
 
 
+We release the scripts to evaluate our model's performance [here](https://github.com/gangiswag/cornstack).
+
 # Usage
 
 **Important**: the query prompt *must* include the following *task instruction prefix*: "Represent this query for searching relevant code" 
@@ -47,3 +49,9 @@ print(query_embeddings)
 code_embeddings = model.encode(codes)
 print(code_embeddings)
 ```
+
+
+
+## Training
+We use a bi-encoder architecture for `CodeRankEmbed`, with weights shared between the text and code encoder. The retriever is contrastively fine-tuned with InfoNCE loss on a high-quality dataset we curated called [CoRNStack](https://gangiswag.github.io/cornstack/). Our encoder is initialized with [Arctic-Embed-M-Long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long), a 137M parameter text encoder supporting an extended context length of 8,192 tokens.
+