Explain the impact of varying the number of attention heads on model performance and computational cost.

Varying the number of attention heads in a Transformer model has a significant impact on both model performance and computational cost. Increasing the number of attention heads generally improves model performance up to a point, as it allows the model to capture more diverse relationships and patterns in the data. Each attention head learns a different set of query, key, and value transformations, which allows it to attend to different aspects of the input sequence. With more heads, the model can capture....