Create README.md

README.md

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