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] - Notebooks
- Google Colab
- Kaggle
Download onnx/model_layer_11.onnx from vllm-sr/Vela-1.0-Encoder-307M-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 82.6 kB
-
https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Embedding/resolve/main/onnx/model_layer_11.onnx
- Command line
-
hf download hf://vllm-sr/Vela-1.0-Encoder-307M-Embedding/onnx/model_layer_11.onnx
-
curl -L -o model_layer_11.onnx https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Embedding/resolve/main/onnx/model_layer_11.onnx
82.6 kB
- Xet hash:
- f1234a70c04a6ef24b2fc64ee15f47ee17eedd5543d7f58bcdfbb64644f1139c
- Size of remote file:
- 82.6 kB
- SHA256:
- 2c566c97c09a77c54d744924dace0c7522ecb5c32b7a167f50158c143c65eeb5
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