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
Is there any native settings to extend the context window to 8k?
#19
by sihanzeng233 - opened
Hi, thanks for releasing the embedding model! Wondering if there's any native settings that can extend the context window from 2k to larger than 8k?
Hi @sihanzeng233 ,
Welcome to Google's Gemma family of open source models, thanks for reaching out to us. The google/embeddinggemma-300m model is having context widow size of 2048 that's the max positional embedding (max_position_embeddings) settings provided to this model. Please refer the config.json file from the file sections to know more about this model settings.
Thanks.