Text Classification
Transformers
ONNX
prompt-injection
prompt-injection-detection
llm-security
bert
jailbreak-detection
Instructions to use nihal4/prompt_injection_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nihal4/prompt_injection_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nihal4/prompt_injection_model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nihal4/prompt_injection_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 352 Bytes
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"is_local": false,
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"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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