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milnico
/
Personality_Cross_Encoder

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:100000
loss:CosineSimilarityLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use milnico/Personality_Cross_Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use milnico/Personality_Cross_Encoder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("milnico/Personality_Cross_Encoder")
    
    sentences = [
        "Believe that unfortunate events occur because of bad luck.",
        "Had someone over for dinner.",
        "Avoid difficult reading material.",
        "Bought or picked flowers."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
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Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Update model metadata to set pipeline tag to the new `text-ranking`

#1 opened over 1 year ago by
tomaarsen
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