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HasinMDG
/
UVC-Deberta-baseline

Text Classification
sentence-transformers
PyTorch
setfit
deberta-v2
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use HasinMDG/UVC-Deberta-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use HasinMDG/UVC-Deberta-baseline with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("HasinMDG/UVC-Deberta-baseline")
    
    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]
  • setfit

    How to use HasinMDG/UVC-Deberta-baseline with setfit:

    from setfit import SetFitModel
    
    model = SetFitModel.from_pretrained("HasinMDG/UVC-Deberta-baseline")
  • Notebooks
  • Google Colab
  • Kaggle
UVC-Deberta-baseline
747 MB
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  • 1 contributor
History: 2 commits
HasinMDG's picture
HasinMDG
Add model
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  • .gitattributes
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  • README.md
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  • added_tokens.json
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  • config.json
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  • config_sentence_transformers.json
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  • model_head.pkl
    395 kB
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  • modules.json
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  • pytorch_model.bin
    735 MB
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
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  • spm.model
    2.46 MB
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  • tokenizer.json
    8.65 MB
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  • tokenizer_config.json
    411 Bytes
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