‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module for asymmetric models & much more. Sparse + Dense = 🔥 hybrid search performance! Details:
1️⃣ Sparse Encoder Models Brand new support for sparse embedding models that generate high-dimensional embeddings (30,000+ dims) where <1% are non-zero:
- Full SPLADE, Inference-free SPLADE, and CSR architecture support - 4 new modules, 12 new losses, 9 new evaluators - Integration with @elastic-co, @opensearch-project, @NAVER LABS Europe, @qdrant, @IBM, etc. - Decode interpretable embeddings to understand token importance - Hybrid search integration to get the best of both worlds
2️⃣ Enhanced Encode Methods & Multi-Processing - Introduce encode_query & encode_document automatically use predefined prompts - No more manual pool management - just pass device list directly to encode() - Much cleaner and easier to use than the old multi-process approach
3️⃣ Router Module & Advanced Training - Router module with different processing paths for queries vs documents - Custom learning rates for different parameter groups - Composite loss logging - see individual loss components - Perfect for two-tower architectures
4️⃣ Comprehensive Documentation & Training - New Training Overview, Loss Overview, API Reference docs - 6 new training example documentation pages - Full integration examples with major search engines - Extensive blogpost on training sparse models
What's next? We would love to hear from the community! What sparse encoder models would you like to see? And what new capabilities should Sentence Transformers handle - multimodal embeddings, late interaction models, or something else? Your feedback shapes our roadmap!