How to select models efficently for fine-tuning?

Hi I was wondering how to select model with ideal accuracy vs training time tradeoff so I collected results of few most popular models and measured their training times of 1 epoch on couple different tasks
Results are here: Loading Google Sheets (open in excel not google sheets)
My idea of using these results is as follows:

  1. Pick baseline model
  2. Look on related plots to see if there are models achiving comparable accuracy but are faster
  3. Stay with baseline or pick better model

My concerne is that 1 epoch of training for model a may be twice as fast as training time of model b but model a may need 3 times more epochs to achieve results of model b. It’s usually not specified in the papers how many epochs were used for fine-tuning so glue or squad results compared to 1 epoch train time may be missleading.

What do you guys think is my idea good or not? How do you select models for your tasks?

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