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
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "extra_special_tokens": [ | |
| "[PAD]", | |
| "[CLS]", | |
| "[SEP]" | |
| ], | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "split_by_punct": false, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "unk_id": 3, | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |