EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hugging Face Blog·Aug 18·5 min read
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
TL;DR
Multi-vector embedding models, specifically ColBERT-style, enhance document retrieval by preserving token-level information, leading to improved accuracy in search results. This approach allows for effective matching of text queries against visual documents without prior OCR processing.
✦ Why It Matters
Start using multi-vector models today to improve the accuracy of your document retrieval systems.
Key Takeaways
- Use MultiVectorEncoder for improved retrieval accuracy.
- Leverage MaxSim for scoring queries against documents.
- Optimize index size with token pooling techniques.
- Explore compatibility with various model checkpoints.
How It Works
Multi-Vector models maintain a vector for each token, allowing for late interaction during scoring. This method uses the MaxSim operator, which sums the highest similarities between query and document tokens, preserving detailed matching information that traditional models lose.