title: Cased VS uncased BERT models in spacy and train data
body: <p>I want to use <code>spacy</code>'s pretrained BERT model for text classification but I'm a little confused about <code>cased/uncased</code> models. I read somewhere that <code>cased</code> 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 <code>cased</code> model must be used. (There is also no <code>uncased</code> model available for German in <code>spacy</code>). </p>
  
  <p>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 <code>.lower()</code> function) or it doesn't make any difference?</p>