Lets say I have mix of words and numbers,which represent for example prices or sizes of objects. The relation/magnitude between the numbers is very important. With normal tokenization it would split those large/less common numbers into smaller pieces (few digits) and concatenate them. Is this not very inefficient and suboptimal, since the transformer has to learn how digits/numbers work and would assign  every digit/combination its own learned vector. Would it make sense to manually convert these numbers and pass in its own vector?
Example:
DATA (Laying carpet 150sqft | price 400$)-> LABEL (okay)
DATA (Carpet type 133 installation with an area of 70m2  | price 8000$)-> LABEL ( Not okay)