Cased VS uncased BERT models in spacy and train data
In simple terms, BERT cased doesn't lowercase the word starting with a capital letter for example in the case of Nouns in the German language.
BERT cased is helpful where the accent plays an important role.
For example schön in German
If we convert schön to schon using BERT uncased, it will have a different meaning.
schön means beautiful whereas schon means already
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?