Is there one model that you use more frequently than others as a base for these disparate fine tuning tasks? Basically, are there any that are particularly flexible?
In general, BERT would be the most common one. RoBERTa is the same model but trained for longer, which turns out to work better. T5 is a larger model, which works better on many tasks but is more expensive.
Thanks for the summary! I'm familiar with BERT, but less so the different variants, so that's quite helpful. I'll take a look at how RoBERTa works.
So far, of the models that run on GPUs with 8-16GiB VRAM XLM-RoBERTa has been the best for these specific tasks. It worked better than the multi-lingual BERT model and language-specific BERT models by quite a wide margin.
Unfortuntaly, distillation of smaller models can take a fair bit of time. However, there is a lot of recent work to make distillation more efficient, e.g. by not just training on the label distributions of of a teacher model, but by also learning to emulate the teacher's attention, hidden layer outputs, etc.