  Transformer models have revolutionized natural language processing with their
unparalleled ability to grasp complex contextual relationships. However, the
vast number of parameters in these models has raised concerns regarding
computational efficiency, environmental impact, and deployability on
resource-limited platforms. To address these challenges, this paper
investigates the application of weight pruning-a strategic reduction of model
parameters based on their significance-as an optimization strategy for
Transformer architectures. Through extensive experimentation, we explore
various pruning methodologies, highlighting their impact on model performance,
size, and computational demands. Our findings suggest that with judicious
selection of pruning hyperparameters, significant reductions in model size are
attainable without considerable compromise on performance. Moreover, when
coupled with post-pruning fine-tuning strategies, some pruned models even
exhibit enhanced generalization capabilities. This work seeks to bridge the gap
between model efficiency and performance, paving the way for more scalable and
environmentally responsible deep learning applications.
