Transformers, pivotal in recent natural language processing (NLP) advancements, rely heavily on the attention mechanism, which remains largely opaque, making tools like BertViz crucial for visualizing and interpreting their processes. BertViz, an open-source tool, visualizes the attention mechanism at neuron, head, and model levels, aiding in explaining model behaviors in transformers such as BERT, GPT-2, and T5, beyond just BERT models. This visualization tool is significant in enhancing model interpretability, especially as transformers are increasingly used in sensitive sectors like healthcare and finance, where understanding model decisions is vital. Despite challenges in correlating attention weights with model outputs, BertViz is a valuable component in the toolkit for explainable artificial intelligence (XAI), providing insights into how models process and generate language, and potentially revealing biases. By visualizing attention, BertViz supports model debugging, performance comparison, and identification of biases, contributing to more transparent and trustworthy AI applications.