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protectai
/
deberta-v3-base-injection-onnx

Text Classification
Transformers
ONNX
English
deberta-v2
prompt-injection
injection
jailbreak
deberta-v3
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use protectai/deberta-v3-base-injection-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use protectai/deberta-v3-base-injection-onnx with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="protectai/deberta-v3-base-injection-onnx")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("protectai/deberta-v3-base-injection-onnx")
    model = AutoModelForSequenceClassification.from_pretrained("protectai/deberta-v3-base-injection-onnx")
  • Notebooks
  • Google Colab
  • Kaggle
deberta-v3-base-injection-onnx
750 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 13 commits
cmdevries's picture
cmdevries
Update README.md
f9d7ed0 verified 4 days ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    1.83 kB
    Update README.md 4 days ago
  • added_tokens.json
    23 Bytes
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  • config.json
    1 kB
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  • model.onnx
    739 MB
    xet
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  • special_tokens_map.json
    173 Bytes
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  • spm.model
    2.46 MB
    xet
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  • tokenizer.json
    8.65 MB
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  • tokenizer_config.json
    1.29 kB
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