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google
/
embeddinggemma-2

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
Model card Files Files and versions
xet
Community
7

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

Update README.md

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hschechter
Google org 4 days ago

Several changes to main body of the model card (before quick start). Includes changes to introduction, overview, architecture table, and benchmark results.

Update README.md4e4b8d86
hschechter changed pull request status to merged 4 days ago

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