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")# pip install -U transformers accelerate # 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
add eval metrics json
Browse files- eval_metrics.json +27 -0
eval_metrics.json
ADDED
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{
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"n": 500,
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"threshold": 0.5,
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"max_seq_length": 3072,
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"is_valid_accuracy": 0.284,
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"category_set_accuracy": 0.226,
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"micro_f1": 0.2872531418312388,
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"macro_f1": 0.23649406281151808,
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"per_category_f1": {
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"DirectInjection": 0.5352112676056338,
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"Jailbreak": 0.15384615384615385,
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"Adversarial": 0.5168539325842697,
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"Extraction": 0.0,
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"Encoding": 0.4225352112676056,
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"Manipulation": 0.0,
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"Smuggling": 0.5,
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"Indirect": 0.0,
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"MultiTurn": 0.0
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},
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"latency_ms_per_example": {
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"mean": 1.7042607069015503,
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"p95": 3.4389287233352657,
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"device": "cuda:0"
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},
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"base_model": "jhu-clsp/mmBERT-base",
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"epochs": 2
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}
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