How to handle different categorical embedding sizes in hold out data set I have a pytorch tabular dataset with zip code as a categorical embedding.  I'm getting great results on the test set.  When I go to run my hold out sample through, it errors out because I have more zip codes in the hold out then what the model was trained on.  

How do I handle this?  In production, the likelihood of seeing a new zip code is high so I need to learn something I can transfer into production.  

Thank you.
One possibility would be to represent the zip codes using some transformation that could be applied to new (unseen) zip codes as well. For example, could you re-represent zip codes as latitude + longitude?