| --- |
| 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 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.48.0.dev0 |
| - Pytorch 2.5.0+cu124 |
| - Datasets 3.1.0 |
| - Tokenizers 0.21.0 |
|
|
