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sengeolab
/
addressfinder

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

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

  • Libraries
  • sentence-transformers

    How to use sengeolab/addressfinder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("sengeolab/addressfinder")
    
    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 sengeolab/addressfinder with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("sengeolab/addressfinder")
    model = AutoModel.from_pretrained("sengeolab/addressfinder")
  • Notebooks
  • Google Colab
  • Kaggle

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