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JMasr
/
balidea-safeguard-attack

Text Classification
Transformers
Safetensors
Spanish
Galician
English
deberta-v2
security
agent
prompt-injection
jailbreak
multilingual
robustness
lora
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use JMasr/balidea-safeguard-attack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use JMasr/balidea-safeguard-attack with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="JMasr/balidea-safeguard-attack")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("JMasr/balidea-safeguard-attack")
    model = AutoModelForSequenceClassification.from_pretrained("JMasr/balidea-safeguard-attack")
  • Notebooks
  • Google Colab
  • Kaggle

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  • .gitattributes
    1.52 kB
    initial commit 2 months ago
  • README.md
    3.49 kB
    Update model card: multilingual capabilities, synthetic noise robustness, and latest metrics about 1 month ago
  • config.json
    994 Bytes
    Upload model about 1 month ago
  • model.safetensors
    738 MB
    xet
    Upload model about 1 month ago
  • special_tokens_map.json
    970 Bytes
    Fix model card and tokenizer_config about 2 months ago
  • tokenizer.json
    8.33 MB
    Upload model about 1 month ago
  • tokenizer_config.json
    1.41 kB
    Fix model card and tokenizer_config about 2 months ago
  • training_report.json
    7.35 kB
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