Sentence Similarity
Core ML
sentence-transformers
multilingual
gemma3_text
feature-extraction
embeddings
gemma
apple-silicon
text-embeddings-inference
Instructions to use shirochenkov90/embeddinggemma-300m-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shirochenkov90/embeddinggemma-300m-coreml with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shirochenkov90/embeddinggemma-300m-coreml") 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
Core ML conversion of google/embeddinggemma-300m (full sentence-transformers pipeline, fp16)
db295fe verified Download tokenizer.json from shirochenkov90/embeddinggemma-300m-coreml: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/shirochenkov90/embeddinggemma-300m-coreml/resolve/main/tokenizer.json
- Command line
-
hf download hf://shirochenkov90/embeddinggemma-300m-coreml/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/shirochenkov90/embeddinggemma-300m-coreml/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 54a250b3b7c81482286dfd8f776cbfdfd7e2889cf6c1674679395abd967cdd60
- Size of remote file:
- 33.4 MB
- SHA256:
- 6852f8d561078cc0cebe70ca03c5bfdd0d60a45f9d2e0e1e4cc05b68e9ec329e
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