Text Classification
Transformers
Safetensors
deberta-v2
prompt-injection-detection
ai-safety
jailbreak-detection
pii-detection
crp
context-relay-protocol
Eval Results (legacy)
text-embeddings-inference
Instructions to use AutoCyberAI/crp-safety-deberta-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AutoCyberAI/crp-safety-deberta-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AutoCyberAI/crp-safety-deberta-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AutoCyberAI/crp-safety-deberta-v1") model = AutoModelForSequenceClassification.from_pretrained("AutoCyberAI/crp-safety-deberta-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# CRP Safety Classifier — DeBERTa-v3-xsmall
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A binary text classifier that labels a prompt as `safe` or `unsafe`. Trained on prompt injection, jailbreak, toxicity, synthetic PII, and adversarial-template examples. Used by `crp.security.injection.InjectionDetector` as the primary ML layer, with a regex pattern library running underneath as a fast pre-filter and fallback.
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# CRP Safety Classifier — DeBERTa-v3-xsmall
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A binary text classifier that labels a prompt as `safe` or `unsafe`. Trained on prompt injection, jailbreak, toxicity, synthetic PII, and adversarial-template examples. Used by `crp.security.injection.InjectionDetector` as the primary ML layer, with a regex pattern library running underneath as a fast pre-filter and fallback.
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