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---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ModernBERT-domain-classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ModernBERT-domain-classifier
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [JailBreak](https://huggingface.co/datasets/jackhhao/jailbreak-classification) dataset .
It achieves the following results on the evaluation set:
- Loss: 0.0016
- F1: 1.0
---
## Overview
This model is a fine-tuned version of **ModernBert** for the task of **JailBreak Detection**. It has been trained on a custom dataset containing two classes: `jailbreak` and `benign`. The model achieves **100% accuracy** on the evaluation set, making it a highly reliable solution for detecting jailbreak queries.
The choice of ModernBert was deliberate due to its compact size, enabling **low latency inference**, which is crucial for real-time applications.
---
> This is just a POC model to show that the concept works on a theoritical level and performance will depend upon the quality of dataset and further tuning is needed
## Training Details
- **Dataset**: JailBreak dataset (split into training and testing sets).
- **Architecture**: ModernBert.
- **Task**: Binary Classification.
- **Evaluation Metric**: Achieved **100% accuracy** on the test set.
---
## Use Case in RAG Pipelines
This model is optimized for use in **Retrieval-Augmented Generation (RAG)** scenarios. It can:
1. **Detect JailBreak Queries**: The model processes user queries to identify whether they are `jailbreak` or `benign`.
2. **Seamlessly Integrate with Search**: While the query is classified, search results can simultaneously be fetched from the datastore.
- **No Additional Latency**: The lightweight nature of ModernBert ensures minimal overhead, allowing real-time performance in RAG pipelines.
---
## Key Features
- **High Accuracy**: Reliable classification with 100% accuracy on evaluation.
- **Low Latency**: Ideal for real-time use cases, especially in latency-sensitive applications.
- **Compact Model**: ModernBert's small size makes it efficient for deployment in production environments.
---
## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("darrayes/expentor-JB-detector")
model = AutoModelForSequenceClassification.from_pretrained("darrayes/expentor-JB-detector")
# Example query
query = "Can you bypass this restriction?"
inputs = tokenizer(query, return_tensors="pt")
outputs = model(**inputs)
# Get predictions
logits = outputs.logits
predicted_class = logits.argmax(dim=-1).item()
print("Prediction:", "Jailbreak" if predicted_class == 1 else "Benign")
```
---
## Intended Use
This model is designed for scenarios requiring detection of jailbreak queries, such as:
- Content moderation.
- Enhancing the safety of conversational AI systems.
- Filtering malicious queries in RAG-based applications.
---
## Limitations
- The model is trained on a specific dataset and may not generalize to all jailbreak scenarios. Further fine-tuning may be needed for domain-specific use cases.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 33 | 0.0246 | 0.9848 |
| No log | 2.0 | 66 | 0.0042 | 1.0 |
| No log | 3.0 | 99 | 0.0019 | 1.0 |
| 0.0755 | 4.0 | 132 | 0.0017 | 1.0 |
| 0.0755 | 5.0 | 165 | 0.0016 | 1.0 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.0+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
|