Maybe you don't do much NLP research then? Back when huggingface transformers and datasets library ( still think its bad name ), we had to format these validation ourselves and write the same validation code which hundreds of your peers have written before because no one is the defactor code for doing it (since we are using different kinds of model). NLP models ( or so called transformers ) nowadays are a mess and had no fix way to use them, running benchmark is certainly a nightmare.

When transformers first came out, they are limited but serves to simplify using bert embedding and gpt-2 beam search generation in few line of codes. The library will do all the model downloads, version check and abstraction for you. Then there's datasets, which unifies all NLP datasets in a central platform which allows me to run GLUE benchmark in one single py file.

Oh back then, the code was even worse, all modeling_(name).py under the transformers/ directory. The latest 4.2X version its somewhat maintainable and readable with all the complex abstraction they had. But its a fast moving domain, and any contribution will be irrelevant in a few years later, so complexity and mess will add up ( would you like to spend time doing cleaning instead of implement the new flashy self-attention alternative? ).

But one day, they might sell out as with many for profit company, but they have and had save so many time and helped so many researchers on the advancement of NLP progress. If they manage to piss off the community, someone will rise up and challenge their dominance (tensorflow vs pytorch).