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daniel-gomm
/
scientific-granite

Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
modernbert
feature-extraction
text-retrieval
scientific-papers
literature-search
arxiv
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use daniel-gomm/scientific-granite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use daniel-gomm/scientific-granite with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("daniel-gomm/scientific-granite")
    
    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
scientific-granite
439 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
daniel-gomm's picture
daniel-gomm
Add ONNX exports
6b6dfaa verified 10 days ago
  • 1_Pooling
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  • assets
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  • deploy
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  • onnx
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  • .gitattributes
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  • LICENSE
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
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  • model.safetensors
    95.3 MB
    xet
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  • modules.json
    277 Bytes
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  • sentence_bert_config.json
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  • tokenizer.json
    3.58 MB
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  • tokenizer_config.json
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