Mask BERT

Mask BERT, and related BERT-based architectures, aim to improve the performance of transformer models, particularly in low-resource scenarios and specialized domains. Current research focuses on adapting BERT for few-shot learning through techniques like selective masking of irrelevant input information and contrastive learning to enhance feature separation. These advancements are significant because they address the data-hungry nature of large language models, enabling more efficient training and improved performance on tasks such as text classification and sentiment analysis, even with limited labeled data. The resulting models find applications in diverse fields, including biomedical semantic search and marketing analysis.