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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
4

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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by hschechter - opened 1 day ago
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hschechter
Google org 1 day ago

Move "model overview" to below bullet points to match formatting for other Gemma models.

Update README.mde438e09a
MaartenGr changed pull request status to merged 1 day ago

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