mxbai-rerank-base-v1
| Property | Value |
|---|---|
| Parameter Count | 184M |
| License | Apache 2.0 |
| Framework | Transformers |
| Precision | FP16 |
What is mxbai-rerank-base-v1?
mxbai-rerank-base-v1 is a powerful reranking model developed by Mixedbread AI, designed to improve search quality by reranking document results. As part of the mxbai-rerank family, this base model offers an excellent balance between performance and computational efficiency, achieving impressive 46.9% NDCG@10 and 72.3% Accuracy@3 on BEIR benchmarks.
Implementation Details
The model is built on the DeBERTa-v2 architecture and optimized for document reranking tasks. It can be easily implemented using sentence-transformers or transformers.js, making it accessible for both Python and JavaScript developers. The model supports batch processing and can be deployed via API or used locally.
- 184M parameters for efficient processing
- FP16 precision for optimal performance
- Compatible with sentence-transformers and transformers.js
- Supports both local deployment and API access
Core Capabilities
- High-quality document reranking with 46.9% NDCG@10
- Effective complement to keyword search systems
- Cross-platform compatibility (Python/JavaScript)
- Batch processing support
- Easy integration with existing search systems
Frequently Asked Questions
Q: What makes this model unique?
The model offers superior reranking performance while maintaining reasonable computational requirements, outperforming many larger models and traditional search methods on BEIR benchmarks. It's particularly effective when combined with keyword search systems.
Q: What are the recommended use cases?
The model excels at reranking search results, document retrieval enhancement, and improving information retrieval systems. It's particularly useful for applications requiring high-quality search results without the computational overhead of larger models.
