As I see it, you need a model you can train quickly so you can do tuning, model selection, and all that.

I have a BERT + SVM + Logistic Regression (for calibration) model that can train 20 models for automatic model selection and calibration in about 3 minutes. I feel like I understand the behavior of it really well.

I've tried fine tuning a BERT for the same task and the shortest model builds take 30 minutes, the training curves make no sense (back in the day I used to be able to train networks with early stopping and get a good one every time) and if I look at arXiv papers it is rare for anyone to have a model selection process with any discipline at all, mainly people use a recipe that sorta-kinda seemed to work in some other paper. People scoff at you if you ask the engineering-oriented question "What training procedure can I use to get a good model consistently?"

Because of that I like classical ML.