People always say “look at BERT” but what if one wants to build ones own sequence-to-vector encoder? Most tutorials on BERT are limited to uses within machine translation (which is a sequence to sequence task), and they spend enormous amount of time on just setting up the dataset and the tokenizer and the mask and a single example configuration of BERT . . . As a guide this is entirely inadequate when it comes to studying sequence-to-vector encodings of the type ErikJ has written about . . . Even a simple sequence-to-sequence example that just talks about the use of the basic APIs and how to investigate various configurations would be useful . . . In other words, the tutorials need to get rid of all data-preparation talk and just work on artificial tensor sequences  . . . It is not hard to build artificial sequences for experimentation purposes and for showing the use of the transformer APIs . . . Not all examples need to be about text sequences . . .