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kiarashmo
/
chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:2184
loss:BatchAllTripletLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use kiarashmo/chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use kiarashmo/chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("kiarashmo/chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss")
    
    sentences = [
        "CC(C)CNCc1ccc(-c2ccccc2S(=O)(=O)N2CCCC2)cc1",
        "C=C1CC2CCC34CC5OC6C(OC7CCC(CC(=O)CC8C(CC9OC(CCC1O2)CC(C)C9=C)OC(CC(O)CN)C8OC)OC7C6O3)C5O4",
        "C[NH+](C)CCC=C1c2ccccc2CCc2ccccc21",
        "CC(C)Cn1cnc2c(N)nc3ccccc3c21"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss / 1_Pooling
321 Bytes
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  • 1 contributor
History: 1 commit
kiarashmo's picture
kiarashmo
chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss
0fd0986 verified over 1 year ago
  • config.json
    321 Bytes
    chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss over 1 year ago