The realm of electrical power distribution relies heavily on transformers, devices that efficiently alter voltage levels for various applications. Within this domain, two primary types stand out: distribution transformers, each playing a crucial role in the intricate network. , Conversely are design

Transformer models have revolutionized natural language processing, achieving state-of-the-art results on a variety of tasks. However, these powerful models are susceptible to inheriting and amplifying biases present in the training data. addressing bias and ensuring fairness in transformer models i