Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
embeddinggemma
code-search
matryoshka
fine-tuned
text-embeddings-inference
Instructions to use jasperan/embeddinggemma-code-search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jasperan/embeddinggemma-code-search with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jasperan/embeddinggemma-code-search") 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 tokenizer.model from jasperan/embeddinggemma-code-search: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/jasperan/embeddinggemma-code-search/resolve/main/tokenizer.model
- Command line
-
hf download hf://jasperan/embeddinggemma-code-search/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/jasperan/embeddinggemma-code-search/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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