Feature Extraction
ONNX
sentence-transformers
onnxruntime
qwen3
embeddings
retrieval
text-embeddings-inference
Instructions to use s-lorin/jina-embeddings-v5-small-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use s-lorin/jina-embeddings-v5-small-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("s-lorin/jina-embeddings-v5-small-onnx") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Jina Embeddings v5 Small ONNX
Prepared ONNX Runtime artifact folder for jinaai/jina-embeddings-v5-text-small.
This repository is a ready-to-download ONNX profile for local retrieval applications that want to run the Jina v5 small embedding model through ONNX Runtime instead of loading the original framework weights directly.
- Base model:
jinaai/jina-embeddings-v5-text-small - Runtime: ONNX Runtime
- Embedding dimension: 1024
- Intended use: local document retrieval, semantic search, and RAG indexing
Files
model.onnx- External ONNX data file referenced by the model
- Tokenizer/config files
onnx_profile.json
Use
Download the repository and point your ONNX Runtime embedder loader at the folder containing model.onnx and the tokenizer files.
from huggingface_hub import snapshot_download
path = snapshot_download("s-lorin/jina-embeddings-v5-small-onnx")
License
This artifact follows the license terms of the upstream Jina model. Review the upstream model card before redistribution or commercial use.
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Model tree for s-lorin/jina-embeddings-v5-small-onnx
Base model
Qwen/Qwen3-0.6B-Base Finetuned
jinaai/jina-embeddings-v5-text-small