Text Classification
Transformers
modernbert
prompt-injection
jailbreak
security
multi-label
llm-guard
encoder
Instructions to use Accuknoxtechnologies/PromptInjection-Encoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Accuknoxtechnologies/PromptInjection-Encoder-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Accuknoxtechnologies/PromptInjection-Encoder-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Accuknoxtechnologies/PromptInjection-Encoder-v1") model = AutoModelForSequenceClassification.from_pretrained("Accuknoxtechnologies/PromptInjection-Encoder-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base_model": "jhu-clsp/mmBERT-base", | |
| "task": "prompt-injection-detection", | |
| "problem_type": "multi_label_classification", | |
| "labels": [ | |
| "DirectInjection", | |
| "Jailbreak", | |
| "Adversarial", | |
| "Extraction", | |
| "Encoding", | |
| "Manipulation", | |
| "Smuggling", | |
| "Indirect", | |
| "MultiTurn" | |
| ], | |
| "max_seq_length": 3072, | |
| "epochs": 10, | |
| "learning_rate": 3e-05, | |
| "threshold": 0.5, | |
| "trained_at": "2026-06-03T18:58:34+00:00" | |
| } |