I cannot agree with OP more. ML model code itself is too often seen as documentation for a paper, in the sense that authors implicitly expect users to go throu

I cannot agree with OP more. ML model code itself is too often seen as documentation for a paper, in the sense that authors implicitly expect users to go through the pre-processing pipeline to find the actual implementation steps. This is because the data handling and pre-processing nitty gritty is not actually interesting from an academic perspective. I cannot count the times I went to look at a paper's implementation source code on a benchmark dataset to find it cut-off annotated sequences to a fixed max length, essentially turning it into a different dataset making comparison to previous work invalid. Good documentation costs time, time academics don't have.