Longformer Model

Longformer is a transformer-based model designed to efficiently process exceptionally long sequences of text or data, overcoming limitations of standard transformers that struggle with inputs exceeding a few hundred tokens. Current research focuses on applying Longformer to diverse tasks, including classifying medical conditions (e.g., depression severity, Alzheimer's disease) from textual or image data, and improving its efficiency through techniques like hierarchical attention and optimized pretraining methods. This work is significant because it enables the application of powerful transformer architectures to problems requiring the analysis of extensive data, such as long documents, longitudinal medical records, and complex visual sequences, leading to advancements in various fields.