What sort of classification labels are you using?  Do you have classification labels for each 512-token text in the training data?
I’m still confused.   If you just want to detect whether a text is a date [or contains a date] or not, then I think BERT is not the best method.
I’ve been working with BERT and my data texts were often longer than 512.   As a compromise, I took just the first 512 tokens in each document, and assumed that the label that related to the full text would also relate to the first 512 tokens.   This did allow the model to learn, but I’m not at all sure it was a valid assumption.
At some point I need to train a model using only data texts that are already ideal lengths, and compare that with the truncated-texts version.
I would be even more worried about a model that used the same label for several sub-sections of text.