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.model from busco-app/embeddinggemma-300m: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/busco-app/embeddinggemma-300m/resolve/main/tokenizer.model
- Command line
-
hf download hf://busco-app/embeddinggemma-300m/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/busco-app/embeddinggemma-300m/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.