base_model:
- Snowflake/snowflake-arctic-embed-m-long
library_name: sentence-transformers
CodeRankEmbed
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 and paper (to be released soon) for more details!
Combine CodeRankEmbed with our re-ranker CodeRankLLM for even higher quality code retrieval.
Performance Benchmarks
| Name | Parameters | CSN (MRR) | CoIR (NDCG@10) |
|---|---|---|---|
| CodeRankEmbed | 137M | 77.9 | 60.1 |
| Arctic-Embed-M-Long | 137M | 53.4 | 43.0 |
| CodeSage-Small | 130M | 64.9 | 54.4 |
| CodeSage-Base | 356M | 68.7 | 57.5 |
| CodeSage-Large | 1.3B | 71.2 | 59.4 |
| Jina-Code-v2 | 161M | 67.2 | 58.4 |
| CodeT5+ | 110M | 74.2 | 45.9 |
| OpenAI-Ada-002 | 110M | 71.3 | 45.6 |
| Voyage-Code-002 | Unknown | 68.5 | 56.3 |
We release the scripts to evaluate our model's performance here.
Usage
Important: the query prompt must include the following task instruction prefix: "Represent this query for searching relevant code"
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("cornstack/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)
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 21 million example high-quality dataset we curated called CoRNStack. Our encoder is initialized with Arctic-Embed-M-Long, a 137M parameter text encoder supporting an extended context length of 8,192 tokens.