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
Any plan for Reranker model?
#45
by Duonglv - opened
Hello Google team,
This model is strong in my language, Vietnamese for similar semantic search.
Could you consider training a reranker model?
Using Embedding or Reranker or both together is much better than only Embedding.
Also, do you have any plans to train a newer Embedding model? Maybe Gemma5-Embed-1B in the next Gemma5 generation.
Thank a lot.