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--- |
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base_model: |
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- Snowflake/snowflake-arctic-embed-m-long |
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--- |
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# CodeRankEmbed |
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`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. |
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# Performance Benchmarks |
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| Name | Parameters | CSN | CoIR | |
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| :-------------------------------:| :----- | :-------- | :------: | |
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| **CodeRankEmbed** | 137M | **77.9** |**60.1** | |
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| CodeSage-Large | 1.3B | 71.2 | 59.4 | |
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| Jina-Code-v2 | 161M | 67.2 | 58.4 | |
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| CodeT5+ | 110M | 74.2 | 45.9 | |
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| Voyage-Code-002 | Unknown | 68.5 | 56.3 | |
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# Usage |
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**Important**: the query prompt *must* include the following *task instruction prefix*: "Represent this query for searching relevant code" |
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```python |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer("cornstack/CodeRankEmbed", trust_remote_code=True) |
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queries = ['Represent this query for searching relevant code: Calculate the n-th Fibonacci number'] |
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codes = ["""def func(n): |
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if n <= 0: |
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return "Input should be a positive integer" |
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elif n == 1: |
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return 0 |
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elif n == 2: |
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return 1 |
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else: |
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a, b = 0, 1 |
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for _ in range(2, n): |
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a, b = b, a + b |
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return b |
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"""] |
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query_embeddings = model.encode(queries) |
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print(query_embeddings) |
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code_embeddings = model.encode(codes) |
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print(code_embeddings) |
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``` |
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