Text Classification
Transformers
Safetensors
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distilbert
jailbreak-detection
text-embeddings-inference
Instructions to use Necent/distilbert-base-uncased-detected-jailbreak with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Necent/distilbert-base-uncased-detected-jailbreak with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Necent/distilbert-base-uncased-detected-jailbreak")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Necent/distilbert-base-uncased-detected-jailbreak") model = AutoModelForSequenceClassification.from_pretrained("Necent/distilbert-base-uncased-detected-jailbreak", device_map="auto") - Notebooks
- Google Colab
- Kaggle
🧠 DistilBERT for Jailbreak Detection
Модель на основе DistilBERT для обнаружения попыток обхода фильтров (jailbreak) в текстах.
📚 Детали модели
- Архитектура: DistilBERT
- Задача: Классификация текста (обнаружение jailbreak)
- Входные данные: Текстовые строки
- Выходные данные: Метка класса (например,
jailbreakилиsafe)
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Model tree for Necent/distilbert-base-uncased-detected-jailbreak
Base model
distilbert/distilbert-base-uncased