Text Classification
Transformers
PyTorch
English
deberta-v2
cybersecurity
ai-security
prompt-injection
jailbreak-detection
llm-security
red-team
prompt-defense
ai-firewall
instruction-override
system-prompt-protection
deberta-v3
multitask-learning
nlp
security-ai
ai-defense
secure-llm
adversarial-ai
detection-system
Eval Results (legacy)
text-embeddings-inference
Instructions to use blackXmask/RedLockX-DeBERTa-v3-Prompt-Injection-Detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blackXmask/RedLockX-DeBERTa-v3-Prompt-Injection-Detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="blackXmask/RedLockX-DeBERTa-v3-Prompt-Injection-Detector")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("blackXmask/RedLockX-DeBERTa-v3-Prompt-Injection-Detector", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0842101930b44ea55b53a7ec21879cc5edaaa3c3c5b804481e9b43d6cd2d939f
- Size of remote file:
- 706 Bytes
- SHA256:
- 025bf42a8d9aa15d15250157c2e90e8709f1bfd676a5db40e3c5c9c1f54833cd
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