TinyBERT Model

TinyBERT is a lightweight version of the BERT language model, designed to improve the efficiency of natural language processing tasks while maintaining reasonable accuracy. Current research focuses on enhancing TinyBERT's performance through techniques like knowledge distillation from larger models (including GPT models) and data augmentation, particularly for specialized domains such as financial sentiment analysis and cyberbullying detection. These advancements address the limitations of deploying large language models in resource-constrained environments, making powerful NLP capabilities accessible for a wider range of applications. Further work explores optimizing TinyBERT's inference speed through dynamic sequence length adjustments to achieve a better balance between accuracy and computational cost.