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tss-deposium
/
gemma300-leaf-embeddings-test

Feature Extraction
PyTorch
sentence-transformers
English
pytorch_leaf_cpu
embeddings
text-embeddings
semantic-search
int8-quantized
knowledge-distillation
leaf
embeddinggemma
Model card Files Files and versions
xet
Community

Instructions to use tss-deposium/gemma300-leaf-embeddings-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use tss-deposium/gemma300-leaf-embeddings-test with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("tss-deposium/gemma300-leaf-embeddings-test")
    
    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
gemma300-leaf-embeddings-test
977 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 8 commits
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tss-deposium
Update README.md
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  • .gitattributes
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  • README.md
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  • config.json
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  • inference_example.py
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  • model_fp32.pt
    500 MB
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  • model_quantized.pt
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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