RADAR-Vicuna-7B

Performs text classification using a RoBERTa-based transformer architecture that has been fine-tuned with adversarial robustness objectives (RADAR training). The model uses masked language modeling pretraining combined with adversarial examples during fine-tuning to learn representations that are resistant to input perturbations and adversarial attacks. It processes raw text through subword tokenization, contextual embedding layers, and a classification head to output class probabilities.

Processes multiple text inputs in parallel through the RoBERTa encoder, accumulating embeddings and computing class probabilities for each sample. Supports configurable confidence thresholds to filter low-confidence predictions, enabling downstream systems to handle uncertain classifications separately. Batching is handled via HuggingFace's pipeline API which manages tokenization, padding, and attention mask generation automatically.

Model is packaged and registered on HuggingFace Model Hub with built-in compatibility for HuggingFace Inference Endpoints and Azure ML deployment pipelines. The model card includes metadata for automatic containerization, API schema generation, and region-specific deployment configuration. Supports both REST API access via HuggingFace's hosted inference service and direct deployment to Azure Container Instances or Azure ML endpoints with minimal configuration.

Supports transfer learning by fine-tuning the pretrained RADAR-Vicuna-7B weights on custom labeled datasets while maintaining adversarial robustness properties. Uses standard supervised fine-tuning with optional adversarial example augmentation during training. The fine-tuning process leverages HuggingFace Trainer API with configurable learning rates, batch sizes, and adversarial training parameters. Preserves the RoBERTa backbone's robustness while adapting the classification head to new label spaces.

Exposes attention weights from the RoBERTa transformer layers, enabling visualization of which input tokens the model attends to when making classification decisions. Supports extraction of attention patterns from multiple layers and heads, and can compute token-level attribution scores (e.g., via gradient-based methods or attention rollout) to identify which words most influence the final classification. Integrates with libraries like Captum or custom attribution scripts for deeper interpretability analysis.