🦉 CodeSearch-ModernBERT-Owl-Plus: High-Performance Sentence-BERT for Code Search

CodeSearch-ModernBERT-Owl-Plus is a high-performance code search model fine-tuned in a Sentence-BERT architecture, based on the pretrained CodeModernBERT-Owl v1.0.

This model is optimized for function-level search within codebases and natural language queries, achieving state-of-the-art results on the MTEB benchmark.

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🛠 Features

• ✅ Fine-tuned in Sentence-BERT format from CodeModernBERT-Owl
• ✅ Supports multiple languages (Python, Java, JavaScript, etc.)
• ✅ Specialized encoder for high-accuracy code search
• ✅ Ideal for multi-stage (dual encoder) retrieval setups
• ✅ Generates rich semantic embeddings for code and queries

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📊 Evaluation on MTEB Benchmark

🏆 Main Scores in MTEB

This model achieved the following main scores (based on NDCG@10):

• CodeSearchNetRetrieval: main_score = 0.8918
• COIR-CodeSearchNetRetrieval: main_score = 0.8013

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🧪 CodeSearchNetRetrieval (MTEB)

Metric	Score
MRR@10	0.8704
NDCG@10	0.8918
MAP@10	0.8704
Recall@10	0.9563
Precision@10	0.0956

This model achieves strong performance across all ranking metrics and demonstrates balanced retrieval capability.

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🧪 COIR-CodeSearchNetRetrieval (MTEB)

Metric	Score
MRR@10	0.7751
NDCG@10	0.8013
MAP@10	0.7751
Recall@10	0.8826
Precision@10	0.0883

Robust and consistent performance is also maintained on the COIR dataset, demonstrating strong generalization.

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📥 Usage Example

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Shuu12121/CodeSearch-ModernBERT-Owl-Plus")
embeddings = model.encode(["binary search function", "def binary_search(arr, target): ..."])

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📝 Conclusion

• ✅ An optimized Sentence-BERT model based on CodeModernBERT-Owl
• ✅ Achieves MRR@10 > 0.87 on MTEB CodeSearchNetRetrieval
• ✅ Ready for integration in production-level code search systems

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📜 License

📄 Apache-2.0

📧 Contact

For questions or inquiries, feel free to reach out: 📧 shun0212114@outlook.jp