Text Classification
Transformers
PyTorch
Safetensors
English
bert
jailbreak
security
moderation
prompt-injection
text-embeddings-inference
Instructions to use lordofthejars/jailbreak-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lordofthejars/jailbreak-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lordofthejars/jailbreak-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lordofthejars/jailbreak-classifier") model = AutoModelForSequenceClassification.from_pretrained("lordofthejars/jailbreak-classifier") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
license: apache-2.0
datasets:
- Open-Orca/OpenOrca
- jackhhao/jailbreak-classification
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- jailbreak
- security
- moderation
- prompt-injection
Jailbreak Classifier
Classifies prompts as jailbreaks or benign. This is a fine-tune checkpoint of bert-base-uncased on the jailbreak-classification dataset.
Training Details
Training Data
Fine-tuned on the jailbreak-classification dataset.
Training Procedure
Training Hyperparameters
Fine-tuning hyper-parameters:
- learning_rate = 5e-5
- train_batch_size = 8
- eval_batch_size = 8
- lr_scheduler_type = linear
- num_train_epochs = 5.0