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bmabir17
/
embeddinggemma-300m

Sentence Similarity
sentence-transformers
LiteRT
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use bmabir17/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use bmabir17/embeddinggemma-300m with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("bmabir17/embeddinggemma-300m")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
embeddinggemma-300m
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  • 1 contributor
History: 3 commits
bmabir17's picture
bmabir17
Update README.md
00a970d verified 14 days ago
  • .gitattributes
    1.52 kB
    initial commit 14 days ago
  • README.md
    7.84 kB
    Update README.md 14 days ago
  • embeddinggemma-300M_seq256_mixed-precision.tflite
    179 MB
    xet
    Upload 3 files 14 days ago
  • embeddinggemma-300M_seq512_mixed-precision.tflite
    179 MB
    xet
    Upload 3 files 14 days ago
  • sentencepiece.model
    4.68 MB
    xet
    Upload 3 files 14 days ago