Encoder Model

Encoder models are fundamental components in many machine learning systems, aiming to efficiently learn and represent complex data such as images, text, and audio. Current research focuses on improving their efficiency, multilingual capabilities, and ability to handle diverse data modalities, exploring architectures like Transformers and employing techniques like contrastive learning and multi-task training. These advancements are driving progress in various applications, including medical image analysis, natural language processing, and speech recognition, by enabling more accurate, efficient, and robust systems.