BGE-Reranker-V2-M3
| Property | Value |
|---|---|
| Parameter Count | 568M |
| License | Apache 2.0 |
| Architecture | XLM-RoBERTa-based |
| Papers | Making Large Language Models A Better Foundation For Dense Retrieval |
What is bge-reranker-v2-m3?
BGE-Reranker-V2-M3 is a lightweight multilingual reranking model designed to improve search quality by directly computing relevance scores between queries and documents. Unlike traditional embedding models, it processes query-document pairs simultaneously to output a similarity score, making it particularly effective for refined search results.
Implementation Details
Built on the BGE-M3 architecture, this model supports efficient inference with optional FP16 acceleration. It implements a direct scoring mechanism where scores can be normalized to a 0-1 range using sigmoid function for easier interpretation.
- Optimized for multilingual processing with strong cross-language capabilities
- Supports both raw and normalized scoring outputs
- Includes efficient batch processing capabilities
- Compatible with Hugging Face Transformers and FlagEmbedding frameworks
Core Capabilities
- Multilingual document reranking
- Direct relevance scoring without embedding computation
- Fast inference with FP16/BF16 support
- Batch processing for multiple query-document pairs
- Integration with popular ML frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient multilingual reranking capabilities while maintaining a relatively small parameter count (568M). It offers direct relevance scoring without the need for separate embeddings, making it particularly efficient for production deployments.
Q: What are the recommended use cases?
The model is ideal for improving search quality in multilingual applications, refining search results from initial retrieval systems, and implementing efficient reranking in production environments where multiple languages need to be supported.
