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dmilush
/
shieldlm-deberta-base

Text Classification
Transformers
Safetensors
English
deberta-v2
prompt-injection
ai-safety
llm-security
jailbreak
deberta-v3
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use dmilush/shieldlm-deberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use dmilush/shieldlm-deberta-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="dmilush/shieldlm-deberta-base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("dmilush/shieldlm-deberta-base")
    model = AutoModelForSequenceClassification.from_pretrained("dmilush/shieldlm-deberta-base")
  • Notebooks
  • Google Colab
  • Kaggle
shieldlm-deberta-base
746 MB
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  • 1 contributor
History: 3 commits
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dmilush
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  • .gitattributes
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  • README.md
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  • calibrated_thresholds.json
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  • config.json
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  • model.safetensors
    738 MB
    xet
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  • tokenizer.json
    8.34 MB
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  • tokenizer_config.json
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