Sentence Similarity
ONNX
sentence-transformers
gemma3_text
feature-extraction
onnxruntime
gemma
embeddinggemma
text-embeddings-inference
Instructions to use cookieshake/embeddinggemma-300m-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cookieshake/embeddinggemma-300m-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cookieshake/embeddinggemma-300m-onnx") 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
Access this EmbeddingGemma ONNX derivative
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This repository contains a Model Derivative of Google's EmbeddingGemma-300M and is distributed under the Gemma Terms of Use (https://ai.google.dev/gemma/terms) and subject to the Gemma Prohibited Use Policy (https://ai.google.dev/gemma/prohibited_use_policy). By requesting access you agree to be bound by both documents, which are included verbatim in this repository as LICENSE and PROHIBITED_USE_POLICY.md.
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