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
Download tokenizer.json from vllm-sr/Vela-1.0-Encoder-307M-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Embedding/resolve/main/tokenizer.json
- Command line
-
hf download hf://vllm-sr/Vela-1.0-Encoder-307M-Embedding/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Embedding/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- daf46a52874fb9b6191b9ae46a0f471904405b611f8e1d580f76c7424d343f89
- Size of remote file:
- 34.4 MB
- SHA256:
- 17f7d8b9518c403d7429ad9eeeabb6eed49c8d3311de8ef4ed5ad811381a2ced
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