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jerwng
/
prompt-injection

Text Classification
Transformers
Safetensors
deberta-v2
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use jerwng/prompt-injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use jerwng/prompt-injection with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="jerwng/prompt-injection")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("jerwng/prompt-injection")
    model = AutoModelForSequenceClassification.from_pretrained("jerwng/prompt-injection")
  • Notebooks
  • Google Colab
  • Kaggle
prompt-injection
746 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
jerwng's picture
jerwng
Upload tokenizer
f5b51d4 verified 6 months ago
  • .gitattributes
    1.52 kB
    initial commit 6 months ago
  • README.md
    5.17 kB
    Upload DebertaV2ForSequenceClassification 6 months ago
  • config.json
    1 kB
    Upload DebertaV2ForSequenceClassification 6 months ago
  • model.safetensors
    738 MB
    xet
    Upload DebertaV2ForSequenceClassification 6 months ago
  • special_tokens_map.json
    970 Bytes
    Upload tokenizer 6 months ago
  • tokenizer.json
    8.65 MB
    Upload tokenizer 6 months ago
  • tokenizer_config.json
    1.51 kB
    Upload tokenizer 6 months ago