Text Classification
Transformers
Safetensors
modernbert
prompt-injection
jailbreak
security
multi-label
llm-guard
encoder
text-embeddings-inference
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
| { | |
| "architectures": [ | |
| "ModernBertForSequenceClassification" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "base_model": "jhu-clsp/mmBERT-base", | |
| "bos_token_id": 2, | |
| "category_thresholds": { | |
| "Adversarial": 0.45, | |
| "DirectInjection": 0.55, | |
| "Encoding": 0.45, | |
| "Extraction": 0.55, | |
| "Indirect": 0.25, | |
| "Jailbreak": 0.05, | |
| "Manipulation": 0.25, | |
| "MultiTurn": 0.7, | |
| "Smuggling": 0.65 | |
| }, | |
| "classifier_activation": "gelu", | |
| "classifier_bias": false, | |
| "classifier_dropout": 0.0, | |
| "classifier_pooling": "mean", | |
| "cls_token_id": 1, | |
| "decoder_bias": true, | |
| "deterministic_flash_attn": false, | |
| "dtype": "float32", | |
| "embedding_dropout": 0.0, | |
| "eos_token_id": 1, | |
| "global_attn_every_n_layers": 3, | |
| "global_rope_theta": 160000, | |
| "gradient_checkpointing": false, | |
| "hidden_activation": "gelu", | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "DirectInjection", | |
| "1": "Jailbreak", | |
| "2": "Adversarial", | |
| "3": "Extraction", | |
| "4": "Encoding", | |
| "5": "Manipulation", | |
| "6": "Smuggling", | |
| "7": "Indirect", | |
| "8": "MultiTurn" | |
| }, | |
| "initializer_cutoff_factor": 2.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1152, | |
| "is_valid_threshold": 0.05, | |
| "label2id": { | |
| "Adversarial": 2, | |
| "DirectInjection": 0, | |
| "Encoding": 4, | |
| "Extraction": 3, | |
| "Indirect": 7, | |
| "Jailbreak": 1, | |
| "Manipulation": 5, | |
| "MultiTurn": 8, | |
| "Smuggling": 6 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "local_attention": 128, | |
| "local_rope_theta": 160000, | |
| "mask_token_id": 4, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "model_type": "modernbert", | |
| "norm_bias": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 22, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "sans_pos", | |
| "problem_type": "multi_label_classification", | |
| "repad_logits_with_grad": false, | |
| "sep_token_id": 1, | |
| "sparse_pred_ignore_index": -100, | |
| "sparse_prediction": false, | |
| "training_provenance": { | |
| "base_model": "jhu-clsp/mmBERT-base", | |
| "epochs": 10, | |
| "labels": [ | |
| "DirectInjection", | |
| "Jailbreak", | |
| "Adversarial", | |
| "Extraction", | |
| "Encoding", | |
| "Manipulation", | |
| "Smuggling", | |
| "Indirect", | |
| "MultiTurn" | |
| ], | |
| "learning_rate": 3e-05, | |
| "max_seq_length": 3072, | |
| "problem_type": "multi_label_classification", | |
| "task": "prompt-injection-detection", | |
| "threshold": 0.5, | |
| "trained_at": "2026-06-03T18:58:34+00:00" | |
| }, | |
| "transformers_version": "4.57.6", | |
| "vocab_size": 256000 | |
| } | |