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NetoAISolutions
/
T-VEC

sentence-transformers
Safetensors
English
qwen2
text-embeddings
telecom
domain-adaptation
triplet-loss
transformer
semantic-search
domain-specific
contrastive-learning
simcse
bio-bert
don’t-stop-pretraining
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use NetoAISolutions/T-VEC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use NetoAISolutions/T-VEC with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("NetoAISolutions/T-VEC", trust_remote_code=True)
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle

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