EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hugging Face Blog·Aug 26·5 min read
Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers
TL;DR
A new approach for finetuning multi-vector embedding models using Sentence Transformers has been developed, achieving superior performance in medical retrieval tasks. The finetuned model outperformed all general-purpose retrieval models tested.
✦ Why It Matters
Start finetuning your multi-vector models today to enhance retrieval performance on your specific datasets.
Key Takeaways
- Utilize MultiVectorEncoder for token-level matching in retrieval tasks.
- Finetune models on domain-specific datasets for better performance.
- Adjust document length configurations to avoid truncation issues.
- Experiment with different loss functions for optimal training results.
How It Works
Multi-vector models score queries against documents by matching tokens individually, preserving fine-grained signals that single-vector models average out. This approach enhances retrieval accuracy, especially in specialized domains.