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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: answerdotai/ModernBERT-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: ModernBERT-domain-classifier |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ModernBERT-domain-classifier |
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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 . |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0016 |
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- F1: 1.0 |
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--- |
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## Overview |
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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. |
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The choice of ModernBert was deliberate due to its compact size, enabling **low latency inference**, which is crucial for real-time applications. |
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> 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 |
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## Training Details |
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- **Dataset**: JailBreak dataset (split into training and testing sets). |
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- **Architecture**: ModernBert. |
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- **Task**: Binary Classification. |
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- **Evaluation Metric**: Achieved **100% accuracy** on the test set. |
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## Use Case in RAG Pipelines |
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This model is optimized for use in **Retrieval-Augmented Generation (RAG)** scenarios. It can: |
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1. **Detect JailBreak Queries**: The model processes user queries to identify whether they are `jailbreak` or `benign`. |
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2. **Seamlessly Integrate with Search**: While the query is classified, search results can simultaneously be fetched from the datastore. |
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- **No Additional Latency**: The lightweight nature of ModernBert ensures minimal overhead, allowing real-time performance in RAG pipelines. |
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## Key Features |
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- **High Accuracy**: Reliable classification with 100% accuracy on evaluation. |
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- **Low Latency**: Ideal for real-time use cases, especially in latency-sensitive applications. |
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- **Compact Model**: ModernBert's small size makes it efficient for deployment in production environments. |
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## Example Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("darrayes/expentor-JB-detector") |
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model = AutoModelForSequenceClassification.from_pretrained("darrayes/expentor-JB-detector") |
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# Example query |
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query = "Can you bypass this restriction?" |
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inputs = tokenizer(query, return_tensors="pt") |
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outputs = model(**inputs) |
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# Get predictions |
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logits = outputs.logits |
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predicted_class = logits.argmax(dim=-1).item() |
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print("Prediction:", "Jailbreak" if predicted_class == 1 else "Benign") |
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``` |
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## Intended Use |
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This model is designed for scenarios requiring detection of jailbreak queries, such as: |
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- Content moderation. |
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- Enhancing the safety of conversational AI systems. |
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- Filtering malicious queries in RAG-based applications. |
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## Limitations |
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- 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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| No log | 1.0 | 33 | 0.0246 | 0.9848 | |
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| No log | 2.0 | 66 | 0.0042 | 1.0 | |
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| No log | 3.0 | 99 | 0.0019 | 1.0 | |
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| 0.0755 | 4.0 | 132 | 0.0017 | 1.0 | |
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| 0.0755 | 5.0 | 165 | 0.0016 | 1.0 | |
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### Framework versions |
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- Transformers 4.48.0.dev0 |
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- Pytorch 2.5.0+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.21.0 |
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