The use of large transformer-based models such as BERT, GPT, and T5 has led to significant advancements in natural language processing. However, these models are computationally expensive, necessitating model compression techniques that reduce their size and complexity while maintaining accuracy. This project investigates and applies knowledge distillation for BERT model compression, specifically focusing on the TinyBERT student model. We explore various techniques to improve knowledge distillation, including experimentation with loss functions, transformer layer mapping methods, and tuning the weights of attention and representation loss and evaluate our proposed techniques on a selection of downstream tasks from the GLUE benchmark. The goal of this work is to improve the efficiency and effectiveness of knowledge distillation, enabling the development of more efficient and accurate models for a range of natural language processing tasks.

本研究使用了Transformer-based模型（如BERT、GPT和T5），并进行了知识蒸馏来进行模型压缩，特别关注TinyBERT学生模型。通过实验不同的损失函数、Transformer层映射方法和注意力和表示损失的权重调整，评估了提出的方法在GLUE基准测试的若干下游任务上的效果，旨在提高知识蒸馏技术的效率和准确性，为各种自然语言处理任务的开发提供更高效和准确的模型。

BERT模型的知识蒸馏改进：损失函数、映射方法和权重调整