‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module & much more. Sparse + Dense = 🔥 hybrid search performance!
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‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module & much more. Sparse + Dense = 🔥 hybrid search performance!
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1️⃣ Sparse Encoder Models - New support for sparse embeddings (30k+ dims, <1% non-zero)
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1️⃣ Sparse Encoder Models - New support for sparse embeddings (30k+ dims, <1% non-zero)
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* Full SPLADE, Inference-free SPLADE, CSR support
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* Full SPLADE, Inference-free SPLADE, CSR support
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* 4 new modules, 12 losses, 9 evaluators
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* 4 new modules, 12 losses, 9 evaluators
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* Integration with elastic, opensearch-project, Qdrant, ibm-granite
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* Integration with elastic, opensearch-project, Qdrant, ibm-granite
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* Decode interpretable embeddings
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* Decode interpretable embeddings
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* Hybrid search integration
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* Hybrid search integration
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2️⃣ Enhanced Encode Methods
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2️⃣ Enhanced Encode Methods
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* encode_query & encode_document with auto prompts
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* encode_query & encode_document with auto prompts
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* Direct device list passing to encode()
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* Direct device list passing to encode()
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* Cleaner multi-processing
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* Cleaner multi-processing
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3️⃣ Router Module & Training
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3️⃣ Router Module & Training
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* Different paths for queries vs documents
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* Different paths for queries vs documents
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* Custom learning rates per parameter group
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* Custom learning rates per parameter group
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* Composite loss logging
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* Composite loss logging
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* Perfect for two-tower architectures
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* Perfect for two-tower architectures
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4️⃣ Documentation & Training
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4️⃣ Documentation & Training
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* New Training/Loss Overview docs
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* New Training/Loss Overview docs
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* 6 training example pages
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* 6 training example pages
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* Search engine integration examples
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* Search engine integration examples
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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
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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
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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!
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I'm incredibly excited to see the community explore sparse embeddings and hybrid search! The interpretability alone makes this a game-changer for understanding what your models are actually doing.
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I'm incredibly excited to see the community explore sparse embeddings and hybrid search! The interpretability alone makes this a game-changer for understanding what your models are actually doing.
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🙏 Thanks to 
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🙏 Thanks to 
@tomaarsen
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tomaarsen
  for this incredible opportunity!
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  for this incredible opportunity!
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‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module & much more. Sparse + Dense = 🔥 hybrid search performance!

1️⃣ Sparse Encoder Models - New support for sparse embeddings (30k+ dims, <1% non-zero)

* Full SPLADE, Inference-free SPLADE, CSR support
* 4 new modules, 12 losses, 9 evaluators
* Integration with elastic, opensearch-project, Qdrant, ibm-granite
* Decode interpretable embeddings
* Hybrid search integration

2️⃣ Enhanced Encode Methods

* encode_query & encode_document with auto prompts
* Direct device list passing to encode()
* Cleaner multi-processing

3️⃣ Router Module & Training

* Different paths for queries vs documents
* Custom learning rates per parameter group
* Composite loss logging
* Perfect for two-tower architectures

4️⃣ Documentation & Training

* New Training/Loss Overview docs
* 6 training example pages
* Search engine integration examples

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!

I'm incredibly excited to see the community explore sparse embeddings and hybrid search! The interpretability alone makes this a game-changer for understanding what your models are actually doing.

🙏 Thanks to @tomaarsen  for this incredible opportunity!


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