We tried many configurations of our network models, but report results with only three configurations of Transformers.

Transformer Type 1: This network is a small to medium-sized network consisting of 4 Transformer layers. Each layer utilizes 8 attention heads with a depth of 512 and a feed-forward depth of 1024.

Transformer Type 2: The second model is small in size, using 2 Transformer layers. The layers utilize 8 attention heads with a depth of 256 and a feed-forward depth of 1024.

Transformer Type 3: The third type of model is minimal, using only 1 Transformer layer. This network utilizes 8 attention heads with a depth of 256 and a feed-forward depth of 512. 
 Question: What neural configurations are explored?
tried many configurations of our network models, but report results with only three configurations Transformer Type 1 Transformer Type 2 Transformer Type 3