Update README.md

README.md

@@ -10,7 +10,7 @@ license: mit
 
 `CodeRankEmbed` is a 137M bi-encoder supporting 8192 context length for code retrieval. It significantly outperforms various open-source and proprietary code embedding models on various code retrieval tasks.  
 
-Check out our [blog post](https://gangiswag.github.io/cornstack/) and [paper (to be released soon)]() for more details!
+Check out our [blog post](https://gangiswag.github.io/cornstack/) and [paper](https://arxiv.org/pdf/2412.01007) for more details!
 
 Combine `CodeRankEmbed` with our re-ranker [`CodeRankLLM`](https://huggingface.co/cornstack/CodeRankLLM) for even higher quality code retrieval.
 
@@ -38,7 +38,7 @@ We release the scripts to evaluate our model's performance [here](https://github
 ```python
 from sentence_transformers import SentenceTransformer
 
-model = SentenceTransformer("cornstack/CodeRankEmbed", trust_remote_code=True)
+model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
 queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
 codes = ['def fact(n):\n if n < 0:\n  raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
 query_embeddings = model.encode(queries)