Feature Extraction
Transformers
Safetensors
sentence-transformers
multilingual
embedding_gemma2
embedding
multimodal-embedding
multimodal
vision
audio
video
image-feature-extraction
audio-feature-extraction
video-feature-extraction
sentence-similarity
Instructions to use google/embeddinggemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/embeddinggemma-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="google/embeddinggemma-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("google/embeddinggemma-2") model = AutoModel.from_pretrained("google/embeddinggemma-2", device_map="auto") - sentence-transformers
How to use google/embeddinggemma-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("google/embeddinggemma-2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Minor issue with the Quickstart
#13
by whither-compendium - opened
Following the Quickstart users are likely to hit errors as it is expected one has pillow installed.
Quick Start
Install the sentence-transformers library:
pip install -U sentence-transformers transformers
Generate text embeddings:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("google/embeddinggemma-2")
query = "What causes the northern lights?"
document = "The northern lights are caused by charged particles from the sun."
query_emb = model.encode(query, prompt_name="SearchQuery")
doc_emb = model.encode(document, prompt_name="Document")
print(model.similarity(query_emb, doc_emb))
Following this from a fresh install and users will likely get errors:
...
ImportError: EmbeddingGemma2Processor requires the PIL library but it was not found in your environment. You can install it with pip:
`pip install pillow`. Please note that you may need to restart your runtime after installation.
To install the required dependencies, run:
pip install -U "sentence-transformers[image]"
Simple fix on user side sure, but I feel dependency should have been included in the Quickstart
Thanks for pointing this out! We have noted this internally. Good catch - since embeddinggemma-2 is a multimodal model, its processor requires pillow, which explains why clean environments run into this ImportError. Appreciate you sharing it!