I am trying to train a model as follows:
!Python -m prodigy train ./tmp_model --ner food_annotations --base-model en_core_web_lg --eval-split 0.10 --config config.cfg
I would like to have a 60/30/10 split for train/validation/evaluation, so that I can train the model on 60% of the data, then run validation on 30% of the data to generalize it and then evaluate the model on 10% of the data. Is there any way to define a validation set in this recipe? Is there another recipe that I can do this with and how would I do that? I have looked through the documentation and other support questions but haven't found an answer, I might not have been able to properly structure my query to find the right previous support question, so I thought I would ask.
Thank you for your help!