Text Classification
Transformers
Safetensors
Korean
roberta
korean
prompt-injection-detection
ai-safety
text-embeddings-inference
Instructions to use kor-prompt-injection-detection/KLUE_roBERTa_based_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kor-prompt-injection-detection/KLUE_roBERTa_based_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kor-prompt-injection-detection/KLUE_roBERTa_based_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kor-prompt-injection-detection/KLUE_roBERTa_based_model") model = AutoModelForSequenceClassification.from_pretrained("kor-prompt-injection-detection/KLUE_roBERTa_based_model") - Notebooks
- Google Colab
- Kaggle
KLUE RoBERTa Based Prompt Injection Detection Model
This repository contains a fine-tuned KLUE RoBERTa based text classification model for Korean prompt injection detection.
Intended Use
This model was developed for a university text mining project. It is intended for academic evaluation and research on Korean prompt injection detection.
The model classifies Korean text into two categories:
normalattack
Evaluation
| Metric | Value |
|---|---|
| Accuracy | 0.9870 |
| Attack Precision | 0.9752 |
| Attack Recall | 0.9882 |
| Attack F1 | 0.9816 |
| Macro F1 | 0.9858 |
| Weighted F1 | 0.9870 |
Detailed evaluation files:
classification_report.txtsummary.csv
Project Repository
https://github.com/3recon/prompt-injection-detection
Limitations
This model is a research artifact for prompt injection detection. It should not be used as a standalone production security system without additional validation.
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Model tree for kor-prompt-injection-detection/KLUE_roBERTa_based_model
Base model
klue/roberta-base