Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

SetFit
/
MiniLM_L3_clinc_oos_plus_distilled

Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community
2

Instructions to use SetFit/MiniLM_L3_clinc_oos_plus_distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use SetFit/MiniLM_L3_clinc_oos_plus_distilled with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("SetFit/MiniLM_L3_clinc_oos_plus_distilled")
    
    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]
  • Transformers

    How to use SetFit/MiniLM_L3_clinc_oos_plus_distilled with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("SetFit/MiniLM_L3_clinc_oos_plus_distilled")
    model = AutoModel.from_pretrained("SetFit/MiniLM_L3_clinc_oos_plus_distilled")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Adding `safetensors` variant of this model

#2 opened over 1 year ago by
SFconvertbot

preparing custom dataset for setfit Model training

#1 opened over 3 years ago by
mahsan30
Company
TOS Privacy About Careers
Website
Models Datasets Spaces Pricing Docs