mxbai-rerank-base-v2
| Property | Value |
|---|---|
| Author | mixedbread-ai |
| Model Type | Reranking Model |
| Latency | 0.67s on A100 GPU |
| BEIR Average Score | 55.57 |
| Repository | Hugging Face |
What is mxbai-rerank-base-v2?
mxbai-rerank-base-v2 is a state-of-the-art reranking model developed by Mixedbread AI, designed to enhance search and retrieval systems. It represents the base variant in their family of reranker models, offering an optimal balance between performance and efficiency. The model achieves impressive benchmarks across multiple domains, including multilingual applications and code search tasks.
Implementation Details
The model implementation follows a sophisticated three-step training process: Guided Reinforcement Prompt Optimization (GRPO), Contrastive Learning, and Preference Learning. It's easily accessible through the mxbai-rerank Python package and can be implemented with just a few lines of code. The model demonstrates exceptional efficiency with a latency of just 0.67 seconds on an A100 GPU.
- State-of-the-art performance metrics with BEIR average of 55.57
- Support for 100+ languages with outstanding English and Chinese performance
- Specialized code search capabilities
- Long-context support for comprehensive document analysis
Core Capabilities
- Multilingual Support: Exceptional performance across 100+ languages, with particular strength in English and Chinese (83.70 score for Chinese)
- Code Search Functionality: Achieves a 31.73 score in code search tasks
- Efficient Processing: Significantly faster than its predecessor with 0.67s latency
- Simple Integration: Easy-to-use Python API for quick implementation
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its combination of high performance and efficiency, supporting both multilingual and code search capabilities while maintaining lower latency compared to larger models. Its three-step training process, including GRPO, makes it particularly robust for real-world applications.
Q: What are the recommended use cases?
This model is ideal for search result reranking, information retrieval systems, multilingual document processing, and code search applications. It's particularly well-suited for scenarios requiring balanced performance and efficiency, especially in multilingual environments.
