Cased VS uncased BERT models in spacy and train data
The difference between "BERT cased" and "BERT uncased" can to finded in different contexts. For example, in the dialogs system, the users rarely put the text in their correct form, so,  is ordinary to find the words in lower case. Maybe, in this case, the BERT in uncased have an advantage.
I want to use spacy's pretrained BERT model for text classification but I'm a little confused about cased/uncased models. I read somewhere that cased models should only be used when there is a chance that letter casing will be helpful for the task. In my specific case: I am working with German texts. And in German all nouns start with the capital letter. So, I think, (correct me if I'm wrong) that this is the exact situation where cased model must be used. (There is also no uncased model available for German in spacy). 
But what must be done with data in this situation?
Should I (while preprocessing train data) leave it as it is (by that I mean not using the .lower() function) or it doesn't make any difference?