mxbai-rerank-xsmall-v1
| Property | Value |
|---|---|
| Parameter Count | 70.8M |
| Model Type | Text Classification / Reranker |
| License | Apache 2.0 |
| Tensor Type | FP16 |
What is mxbai-rerank-xsmall-v1?
mxbai-rerank-xsmall-v1 is the smallest model in MixedBread AI's family of reranker models, designed to enhance search quality through efficient document reranking. Despite its compact size, it achieves impressive performance with 43.9 NDCG@10 and 70.0% Accuracy@3 on BEIR benchmarks, outperforming many larger models.
Implementation Details
Built on DeBERTa-v2 architecture, this model is optimized for both performance and efficiency. It can be easily implemented using sentence-transformers or transformers.js, making it versatile for both Python and JavaScript environments. The model uses FP16 precision to maintain efficiency while preserving accuracy.
- Efficient 70.8M parameter architecture
- Optimized for cross-encoding document ranking
- Supports both CPU and GPU inference
- Available through API and local deployment options
Core Capabilities
- Document reranking for search enhancement
- Query-document relevance scoring
- Integration with existing search systems
- Cross-platform support (Python/JavaScript)
- Batch processing capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model offers an exceptional balance between size and performance, achieving competitive results while being significantly smaller than alternatives. It's particularly effective when combined with keyword search systems and can outperform some semantic search approaches.
Q: What are the recommended use cases?
The model is ideal for enhancing search systems, document retrieval applications, and content recommendation engines. It's particularly suited for scenarios where computational resources are limited but high-quality reranking is required.
