The project centres on building a production-ready text-classification pipeline that leverages modern deep-learning techniques. I have a labelled dataset and need end-to-end code that ingests the text, handles cleaning and tokenisation, and trains an accurate classifier. Python is the preferred language; using PyTorch, TensorFlow or another mainstream framework is fine as long as the solution is reproducible and easy to extend. Key deliverables: • Well-commented source code (data loading, model, training loop, evaluation) • Clear instructions to run training on a fresh machine (README or notebook) • Metrics report showing accuracy, precision, recall and F1 on a held-out set • Exported model weights and a small inference script or API endpoint for batch prediction Trained embeddings (e.g., BERT, RoBERTa) are acceptable, but if you opt for lighter models please ensure comparable performance. Provide any custom preprocessing utilities you write. When you reply, focus on your experience with similar NLP or text-classification projects—model choices you have implemented, datasets you have handled, and any production deployments you have managed.