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bastionsoft
/
binary-bastion-prompt-protection-mdeberta-v3-base-v1

Text Classification
Transformers
ONNX
Safetensors
deberta-v2
prompt-injection
jailbreak-detection
prompt-injection-detection
llm-security
guardrails
multilingual
mdeberta
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use bastionsoft/binary-bastion-prompt-protection-mdeberta-v3-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use bastionsoft/binary-bastion-prompt-protection-mdeberta-v3-base-v1 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="bastionsoft/binary-bastion-prompt-protection-mdeberta-v3-base-v1")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("bastionsoft/binary-bastion-prompt-protection-mdeberta-v3-base-v1")
    model = AutoModelForSequenceClassification.from_pretrained("bastionsoft/binary-bastion-prompt-protection-mdeberta-v3-base-v1", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle

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  • onnx
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  • .gitattributes
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  • LICENSE.md
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  • README.md
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    Model card adjustments 4 months ago
  • added_tokens.json
    23 Bytes
    release: final 4 months ago
  • config.json
    952 Bytes
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  • model.safetensors
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    xet
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  • special_tokens_map.json
    286 Bytes
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  • spm.model
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    xet
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  • temperature.json
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  • tokenizer.json
    16.4 MB
    xet
    release: final 4 months ago
  • tokenizer_config.json
    19.7 kB
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