Enhancing RoBERTa Robustness through Adversarial Training

### Feature request The goal of this feature is to implement Adversarial Training for the RoBERTa model to enhance its robustness against adversarial examples. Adversarial training involves generating perturbed inputs (adversarial examples) during the training phase, allowing the model to learn how to withstand such attacks. This improves the model's generalization and performance on unseen data. # Implementation Overview: ### **Adversarial Example Generation:** 1. Methods: Utilize techniques like FGSM (Fast Gradient Sign Method) or PGD (Projected Gradient Descent) 2. Integration: Modify the training loop to incorporate a step that generates adversarial examples for each batch of data. Loss Function Adjustment: Combine the traditional loss (e.g., cross-entropy) with a loss calculated on the adversarial examples. This can be done using a weighted sum to balance the two components. Training Procedure: Modify the training loop: For each epoch: 1. Generate adversarial examples from the input batch. 2. Compute the loss on both clean and adversarial examples. 3. Update the model weights based on the combined loss. ***Hyperparameter Tuning:*** Introduce parameters such as the adversarial strength (epsilon) and the weighting factor ( 𝜆 λ) to adjust the training dynamics and effectiveness. ### Evaluation Metrics: Evaluate model performance using metrics like accuracy, precision, recall, and F1-score on both clean and adversarial datasets to measure the robustness improvements. # Link to Paper: [Adversarial Training for Natural Language Processing](https://arxiv.org/abs/1906.05955) [Adversarial Examples for Evaluating Reading Comprehension Systems](https://arxiv.org/abs/1904.07236) [Adversarial Training for Large Neural Language Models](https://arxiv.org/abs/1909.03247) [Towards Robustness Against Adversarial Attacks in Natural Language Processing](https://arxiv.org/abs/2002.07677) [Adversarial Training with Natural Language Processing](https://arxiv.org/abs/2103.09582) #…