BGE-M3
| Property | Value |
|---|---|
| Author | BAAI |
| License | MIT |
| Paper | View Paper |
| Dimension | 1024 |
| Max Sequence Length | 8192 tokens |
What is BGE-M3?
BGE-M3 is a groundbreaking embedding model that excels in three key areas: Multi-Functionality, Multi-Linguality, and Multi-Granularity. It represents a significant advancement in text embedding technology, capable of processing content across more than 100 languages while supporting multiple retrieval methods simultaneously.
Implementation Details
The model implements three distinct retrieval functionalities: dense retrieval for single vector embeddings, sparse retrieval for lexical matching, and multi-vector retrieval using ColBERT architecture. It's built on XLM-RoBERTa architecture with extended context length support up to 8192 tokens.
- Dense retrieval generates single vector embeddings for efficient similarity search
- Sparse retrieval provides token-level weights similar to BM25
- Multi-vector retrieval enables fine-grained text matching using multiple vectors
Core Capabilities
- Processes inputs from short sentences to long documents (up to 8192 tokens)
- Supports 100+ languages with state-of-the-art performance
- Unified architecture for multiple retrieval methods
- Self-knowledge distillation for improved performance
- Efficient batching for long text processing
Frequently Asked Questions
Q: What makes this model unique?
BGE-M3's uniqueness lies in its ability to combine three different retrieval methods (dense, sparse, and multi-vector) in a single model while supporting over 100 languages and handling long documents efficiently. This versatility makes it particularly valuable for RAG applications and cross-lingual information retrieval.
Q: What are the recommended use cases?
The model is ideal for building multilingual search systems, document retrieval applications, and RAG pipelines. It's particularly effective when used in hybrid retrieval setups combined with re-ranking, making it suitable for production-grade information retrieval systems.
