Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
mirror
embeddinggemma
busco
text-embeddings-inference
Instructions to use busco-app/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use busco-app/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("busco-app/embeddinggemma-300m") 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
Mirror google/embeddinggemma-300m at 57c266a740f537b4dc058e1b0cda161fd15afa75 (weights unmodified)
80d1ba7 verified Download tokenizer.json from busco-app/embeddinggemma-300m: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/busco-app/embeddinggemma-300m/resolve/main/tokenizer.json
- Command line
-
hf download hf://busco-app/embeddinggemma-300m/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/busco-app/embeddinggemma-300m/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 54a250b3b7c81482286dfd8f776cbfdfd7e2889cf6c1674679395abd967cdd60
- Size of remote file:
- 33.4 MB
- SHA256:
- 6852f8d561078cc0cebe70ca03c5bfdd0d60a45f9d2e0e1e4cc05b68e9ec329e
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