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:
- 60309f3b06b6aa9fc8dd613dc39af15facbd36421e602fbd0f2411f5b2bb185a
- Size of remote file:
- 565 MB
- SHA256:
- 7fa886a0b5a8d9e062d6a6a49b78e4250b33548c97f74c4536901cb4fcbc7ac9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.