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", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Accuknoxtechnologies/PromptInjection-Encoder-v1") model = AutoModelForSequenceClassification.from_pretrained("Accuknoxtechnologies/PromptInjection-Encoder-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,045 Bytes
34d4a52 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"n": 500,
"calibrated": true,
"threshold": "per-class",
"is_valid_threshold": 0.05,
"category_thresholds": {
"DirectInjection": 0.55,
"Jailbreak": 0.05,
"Adversarial": 0.45,
"Extraction": 0.55,
"Encoding": 0.45,
"Manipulation": 0.25,
"Smuggling": 0.65,
"Indirect": 0.25,
"MultiTurn": 0.7
},
"max_seq_length": 3072,
"is_valid_accuracy": 0.968,
"category_set_accuracy": 0.688,
"micro_f1": 0.7893805309734513,
"macro_f1": 0.7848505189708921,
"per_category_f1": {
"DirectInjection": 0.8235294117647058,
"Jailbreak": 0.7368421052631579,
"Adversarial": 0.855072463768116,
"Extraction": 0.7652173913043478,
"Encoding": 0.7516778523489933,
"Manipulation": 0.6785714285714286,
"Smuggling": 0.9256198347107438,
"Indirect": 0.8382352941176471,
"MultiTurn": 0.6888888888888889
},
"latency_ms_per_example": {
"mean": 1.7930222675204277,
"p95": 1.8397919833660126,
"device": "cuda:0"
},
"base_model": "jhu-clsp/mmBERT-base",
"epochs": 10
} |