title: Siamese Neural Network with transformers and the embeddings problem
body: <p>I am working with a Siamese network adaptation for a ranking task as I have sequential data, the first network (for the queries) has a transformer (as it is simple it is an encoder) followed by an embedding layer, the second network (for the indices) is simply a fully connected followed by an embedding layer identical to the previous network. At the end of everything, it uses the similarity of the cosine to make the ranking between the output of both networks. It gives good results but I am interested in knowing what is happening at the output of both networks. <strong>Any advice?</strong></p>
  <p>PD: I was thinking of using a t-SNE to visualize the output of the embedding layers in 2D - 3D but I am mainly interested in knowing how the clusters are organized in order to make the appropriate modifications in the network.</p>