‼️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:
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‼️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:
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1️⃣ Sparse Encoder Models
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1️⃣ Sparse Encoder Models
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Brand new support for sparse embedding models that generate high-dimensional embeddings (30,000+ dims) where <1% are non-zero:
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Brand new support for sparse embedding models that generate high-dimensional embeddings (30,000+ dims) where <1% are non-zero:
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- Full SPLADE, Inference-free SPLADE, and CSR architecture support
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- Full SPLADE, Inference-free SPLADE, and CSR architecture support
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- 4 new modules, 12 new losses, 9 new evaluators
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- 4 new modules, 12 new losses, 9 new evaluators
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- Integration with 
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- Integration with 
@elastic-co
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elastic-co
, 
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, 
@opensearch-project
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opensearch-project
, 
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, 
@NAVER
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NAVER
 LABS Europe, 
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 LABS Europe, 
@qdrant
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qdrant
, 
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, 
@IBM
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IBM
, etc.
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, etc.
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- Decode interpretable embeddings to understand token importance
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- Decode interpretable embeddings to understand token importance
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- Hybrid search integration to get the best of both worlds
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- Hybrid search integration to get the best of both worlds
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2️⃣ Enhanced Encode Methods & Multi-Processing
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2️⃣ Enhanced Encode Methods & Multi-Processing
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- Introduce encode_query & encode_document automatically use predefined prompts
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- Introduce encode_query & encode_document automatically use predefined prompts
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- No more manual pool management - just pass device list directly to encode()
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- No more manual pool management - just pass device list directly to encode()
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- Much cleaner and easier to use than the old multi-process approach
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- Much cleaner and easier to use than the old multi-process approach
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3️⃣ Router Module & Advanced Training
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3️⃣ Router Module & Advanced Training
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- Router module with different processing paths for queries vs documents
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- Router module with different processing paths for queries vs documents
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- Custom learning rates for different parameter groups
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- Custom learning rates for different parameter groups
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- Composite loss logging - see individual loss components
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- Composite loss logging - see individual loss components
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- Perfect for two-tower architectures
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- Perfect for two-tower architectures
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4️⃣ Comprehensive Documentation & Training
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4️⃣ Comprehensive Documentation & Training
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- New Training Overview, Loss Overview, API Reference docs
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- New Training Overview, Loss Overview, API Reference docs
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- 6 new training example documentation pages
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- 6 new training example documentation pages
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- Full integration examples with major search engines
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- Full integration examples with major search engines
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- Extensive blogpost on training sparse models
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- Extensive blogpost on training sparse models
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Read the comprehensive blogpost about training sparse embedding models: 
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Read the comprehensive blogpost about training sparse embedding models: 
https://huggingface.co/blog/train-sparse-encoder
https://huggingface.co/blog/train-sparse-encoder
link
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See the full release notes here: 
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See the full release notes here: 
https://github.com/UKPLab/sentence-transformers/releases/v5.0.0
https://github.com/UKPLab/sentence-transformers/releases/v5.0.0
link
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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!
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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!
‼️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

Read the comprehensive blogpost about training sparse embedding models: https://huggingface.co/blog/train-sparse-encoder

See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v5.0.0

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!
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