9
Backend Export
⚙
Export Sentence Transformer models to accelerated backends
AI & ML interests
In the following you find models tuned to be used for sentence / text embedding generation. They can be used with the sentence-transformers package.
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Add `text-embeddings-inference` tag & snippet
#14 opened 8 days ago
by
alvarobartt
Add `text-embeddings-inference` tag & snippet
#3 opened 8 days ago
by
alvarobartt
Add `text-embeddings-inference` tag & snippet
#4 opened 8 days ago
by
alvarobartt
Add `text-embeddings-inference` tag & snippet
#4 opened 8 days ago
by
alvarobartt
Add `text-embeddings-inference` tag & snippet
#8 opened 8 days ago
by
alvarobartt
Add `text-embeddings-inference` tag & snippet
#8 opened 8 days ago
by
alvarobartt
Add `text-embeddings-inference` tag & snippet
#4 opened 8 days ago
by
alvarobartt
Add `text-embeddings-inference` tag & snippet
#40 opened 9 days ago
by
alvarobartt
tomaarsen
updated
a
Space
8 days ago
Inference provider code snippet needs update
1
#127 opened 13 days ago
by
0xarnav
Post
3774
😎 I just published Sentence Transformers v5.1.0, and it's a big one. 2x-3x speedups of SparseEncoder models via ONNX and/or OpenVINO backends, easier distillation data preparation with hard negatives mining, and more:
1️⃣ Faster ONNX and OpenVINO backends for SparseEncoder models
Usage is as simple as
2️⃣ New
This new output format is immediately compatible with the MarginMSELoss and SparseMarginMSELoss for training SentenceTransformer, CrossEncoder, and SparseEncoder losses.
3️⃣ Gathering across devices
When doing multi-GPU training using a loss that has in-batch negatives (e.g. MultipleNegativesRankingLoss), you can now use
4️⃣ Trackio support
If you also upgrade
5️⃣ MTEB Documentation
We've added some documentation on evaluating SentenceTransformer models properly with MTEB. It's rudimentary as the documentation on the MTEB side is already great, but it should get you started.
Plus many more smaller features & fixes (crash fixes, compatibility with datasets v4, FIPS compatibility, etc.).
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v5.1.0
Big thanks to all of the contributors for helping with the release, many of the features from this release were proposed by others. I have a big list of future potential features that I'd love to add, but I'm
1️⃣ Faster ONNX and OpenVINO backends for SparseEncoder models
Usage is as simple as
backend="onnx" or backend="openvino" when initializing a SparseEncoder to get started, but I also included utility functions for optimization, dynamic quantization, and static quantization, plus benchmarks.2️⃣ New
n-tuple-scores output format from mine_hard_negativesThis new output format is immediately compatible with the MarginMSELoss and SparseMarginMSELoss for training SentenceTransformer, CrossEncoder, and SparseEncoder losses.
3️⃣ Gathering across devices
When doing multi-GPU training using a loss that has in-batch negatives (e.g. MultipleNegativesRankingLoss), you can now use
gather_across_devices=True to load in-batch negatives from the other devices too! Essentially a free lunch, pretty big impact potential in my evals.4️⃣ Trackio support
If you also upgrade
transformers, and you install trackio with pip install trackio, then your experiments will also automatically be tracked locally with trackio. Just open up localhost and have a look at your losses/evals, no logins, no metric uploading.5️⃣ MTEB Documentation
We've added some documentation on evaluating SentenceTransformer models properly with MTEB. It's rudimentary as the documentation on the MTEB side is already great, but it should get you started.
Plus many more smaller features & fixes (crash fixes, compatibility with datasets v4, FIPS compatibility, etc.).
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v5.1.0
Big thanks to all of the contributors for helping with the release, many of the features from this release were proposed by others. I have a big list of future potential features that I'd love to add, but I'm
ValueError when using load_dataset
1
#1 opened about 1 month ago
by
tuhanasinan
Adding `safetensors` variant of this model
#2 opened about 1 month ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened about 1 month ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened about 1 month ago
by
SFconvertbot
Adding `safetensors` variant of this model
#3 opened about 1 month ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened about 1 month ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened over 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#6 opened 4 months ago
by
SFconvertbot
arthurbresnu
posted
an
update
about 2 months ago
Post
2188
‼️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!
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!