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Teradata
/
embeddinggemma-300m

Feature Extraction
sentence-transformers
ONNX
gemma3_text
teradata
byom
embeddings
gemma
gemma3
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Teradata/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Teradata/embeddinggemma-300m with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Teradata/embeddinggemma-300m")
    
    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
embeddinggemma-300m / onnx
1.74 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
sasha-smirnov's picture
sasha-smirnov
Initial publish via td-embeddings
ac61f3b verified 13 days ago
  • model-ffn_skip.onnx
    508 MB
    xet
    Initial publish via td-embeddings 13 days ago
  • model-fp32.onnx
    1.23 GB
    xet
    Initial publish via td-embeddings 13 days ago