Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
text-embeddings-inference
Eval Results
Instructions to use google/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use google/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("google/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] - Inference
- Notebooks
- Google Colab
- Kaggle
Question: mobile RAG performance?
#43
by 3morixd - opened
We use embedding models for mobile RAG — indexing personal documents on phone.
Question: what's the retrieval quality after 4-bit quantization? Does it degrade more for multilingual text?
Our tests: <2% quality loss at 4-bit for English, but we need more data for other languages.
- Dispatch AI (FZE), Sharjah UAE
Hi @3morixd
Since quality loss depends heavily on your specific use case, I recommend trying Matryoshka Representation. It's our official feature designed to help you balance speed and accuracy perfectly.