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CSI-lab
/
US-State-Law-EmbeddingGemma-2

Feature Extraction
Transformers
Safetensors
sentence-transformers
English
embedding_gemma2
embedding
legal
law
WA
multimodal-embedding
multimodal
sentence-similarity
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use CSI-lab/US-State-Law-EmbeddingGemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use CSI-lab/US-State-Law-EmbeddingGemma-2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="CSI-lab/US-State-Law-EmbeddingGemma-2")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModel
    
    processor = AutoProcessor.from_pretrained("CSI-lab/US-State-Law-EmbeddingGemma-2")
    model = AutoModel.from_pretrained("CSI-lab/US-State-Law-EmbeddingGemma-2", device_map="auto")
  • sentence-transformers

    How to use CSI-lab/US-State-Law-EmbeddingGemma-2 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("CSI-lab/US-State-Law-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
US-State-Law-EmbeddingGemma-2
1.53 GB
Ctrl+K
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  • 1 contributor
History: 7 commits
CSI-lab's picture
CSI-lab
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