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fpc
/
bge-micro-smiles

Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:3210255
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use fpc/bge-micro-smiles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use fpc/bge-micro-smiles with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("fpc/bge-micro-smiles")
    
    sentences = [
        "donepezil hydrochloride monohydrate",
        "Cn1nccc1[C@H]1CC[C@H](O[Si](C)(C)C(C)(C)C)C[C@@H]1OC(=O)c1ccccc1",
        "COc1cc2c(cc1OC)C(=O)C(CC1CCN(Cc3ccccc3)CC1)C2.Cl.O",
        "C(=O)(OC)C1=CC=C(C=C1)CC(C)=O"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
bge-micro-smiles / onnx
156 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 1 commit
ferris
Add onnx models
ef633dd over 1 year ago
  • model.onnx
    69 MB
    xet
    Add onnx models over 1 year ago
  • model_O3.onnx
    69 MB
    xet
    Add onnx models over 1 year ago
  • model_quint8_avx2.onnx
    17.5 MB
    xet
    Add onnx models over 1 year ago