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minar-svn
/
typic-bert

Text Classification
Transformers
Safetensors
English
decision-model
zero-shot-classification
routing
guardrails
calibrated
modernbert
Model card Files Files and versions
xet
Community

Instructions to use minar-svn/typic-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use minar-svn/typic-bert with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="minar-svn/typic-bert")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("minar-svn/typic-bert", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle

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  • demo
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  • .gitattributes
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  • README.md
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    README: add Demo section 3 days ago
  • decision_config.json
    157 Bytes
    Upload typic-bert decision model 3 days ago
  • eval_report.json
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  • model.safetensors
    1.58 GB
    xet
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  • modeling_typic.py
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  • tokenizer.json
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  • tokenizer_config.json
    351 Bytes
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