Original BERT

BERT, a bidirectional transformer-based language model, revolutionized natural language processing by achieving state-of-the-art results on various tasks through self-supervised pre-training and transfer learning. Current research focuses on improving BERT's efficiency and performance, exploring enhancements such as modified masking strategies, optimized decoder architectures, and dynamic layer selection during inference to reduce computational costs while maintaining accuracy. These advancements are significant because they broaden BERT's applicability, enabling its deployment on resource-constrained devices and improving its effectiveness across diverse downstream tasks, including sentiment analysis, hate speech detection, and legal text processing.