mxbai-rerank-large-v2
| Property | Value |
|---|---|
| Author | mixedbread-ai |
| Model URL | https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2 |
| BEIR Average Score | 57.49 |
| Latency (A100) | 0.89s |
What is mxbai-rerank-large-v2?
mxbai-rerank-large-v2 is a state-of-the-art reranking model developed by Mixedbread AI, designed to optimize search and retrieval tasks across multiple languages. This large-scale model represents the pinnacle of their reranker family, offering superior performance compared to its base counterpart while maintaining efficient processing speeds.
Implementation Details
The model implements a sophisticated three-step training process: GRPO (Guided Reinforcement Prompt Optimization), Contrastive Learning, and Preference Learning. It achieves impressive benchmark results with a BEIR average of 57.49, particularly excelling in Chinese language tasks with a score of 84.16. The model maintains a reasonable latency of 0.89 seconds on an A100 GPU.
- Multilingual support for 100+ languages
- Exceptional performance in English and Chinese
- Code search capability with 32.05 score
- Long-context support for comprehensive document analysis
- Easy integration through mxbai-rerank Python package
Core Capabilities
- Advanced document reranking for improved search results
- Multilingual processing with specialized optimization
- Efficient processing with reasonable latency
- Code search functionality
- Simple API integration
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its combination of state-of-the-art performance across multiple languages, particularly in English and Chinese, while maintaining efficient processing speeds. Its three-step training process incorporating GRPO, Contrastive Learning, and Preference Learning sets it apart from traditional reranking models.
Q: What are the recommended use cases?
The model is ideal for search systems requiring multilingual support, code search applications, and any scenario demanding precise document ranking. It's particularly effective for English and Chinese content, making it suitable for international search applications and content organization systems.
