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databio
/
sbert-encode-cellines-tuned

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1128
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community
2

Instructions to use databio/sbert-encode-cellines-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use databio/sbert-encode-cellines-tuned with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("databio/sbert-encode-cellines-tuned")
    
    sentences = [
        "connective tissue cell",
        "GM18507",
        "GM18526",
        "GM08714"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Add new SentenceTransformer model with an onnx backend

#2 opened over 1 year ago by
ClaudeHu05

Add new SentenceTransformer model with an onnx backend

#1 opened over 1 year ago by
ClaudeHu05
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