Sentence Similarity
sentence-transformers
ONNX
Safetensors
modernbert
semantic-router
vela
matryoshka
text-embeddings-inference
Instructions to use vllm-sr/Vela-1.0-Encoder-307M-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vllm-sr/Vela-1.0-Encoder-307M-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vllm-sr/Vela-1.0-Encoder-307M-Embedding") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
27 days ago
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Update Hugging Face org references: llm-semantic-router → vllm-sr
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Expand encoder retrieval coverage with matching exports and measured scores
Verified signatureUpdate the encoder and matching exports, refresh only product-page scores, preserve the opt-in AMD helper, and retain complete measured trade-offs in the separate evaluation evidence.972c180 -
Update encoder with additional Chinese retrieval training and matching exports
Verified signatureUpdate the encoder and matching exports, refresh only product-page scores, preserve the opt-in AMD helper, and retain complete measured trade-offs in the separate evaluation evidence.bc1093b
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Add optional qualified AMD SDPA runtime helper
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Update retrieval-distilled encoder and matching exports
Verified signatureUpdate the encoder and matching exports, refresh only product-page scores, and retain detailed evaluation evidence separately. Small classification, retrieval and long-document point gains, alongside clustering and representation-grid regressions, remain visible in the measured results.75c25cd -
Refine retrieval and multi-resolution embeddings
Verified signatureUpdate the encoder and matching exports, refresh only product-page scores, and retain detailed evaluation evidence separately. Long-document, clustering and cross-language trade-offs remain visible in the measured results.9952e61 -
Improve multilingual classification and long-document retrieval
Verified signatureUpdate the encoder and matching exports, refresh only product-page scores, and retain detailed evaluation evidence separately. Short-retrieval regressions remain visible in the measured results.27cf105 -
Restore product model card and update evaluation scores
Verified signatureRestore the original product copy and quick start. Replace only the Evaluation section with the verified current scores against mmBERT. Detailed evaluation records remain separate.604c75c -
Publish R3 interim encoder: classification and long retrieval gains with disclosed tradeoffs
Verified signatureAtomic native weights, tokenizer/config, eight standard/CK ONNX graphs and shared weights, model card, and complete bounded evaluation evidence. Native FP32 scores compare primarily with mmBERT. Short retrieval and early-exit regressions remain visible; this is not a full-MMTEB or universal-superiority claim.ae3a92a
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Improve multilingual retrieval and semantic matching
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Improve multilingual retrieval and semantic matching
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Add ROCm-accelerated ONNX representations
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Add portable FP32 ONNX models
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Add original mmBERT evaluation comparison for Vela Embedding
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Simplify Vela quick starts and model cards
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Release Vela on the unified Encoder base
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Unify Vela model cards with Semantic Router branding
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Release Vela 1.0 Embedding
Verified signatureMultilingual retrieval with flexible representations and long-context support.5e639f1 -
initial commit
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