Hi, I'm having issues when adding a new entity type to previously trained model. Let's say I have an entity "A" that I want to add to the en_core_web_lg model. This entity is completely new, comprising of words that the model very rarely recognizes in any of its own entity categories. I created annotations for this entity using ner.manual and then used the following command to train and update the base model:
prodigy train A-model --ner annotation_dataset -m en_core_web_lg -L -V
The newly trained model "A-model" does not seem to remember any of the previous en_core_web_lg entities when evaluated using the print stream recipe and also when I verified it using spacy.
My ultimate goal is to add one more NER category to this model (so two in total, A plus a new one, B, added to en_core_web_lg) while not losing any of the native categories. I was planning on doing this iteratively, ie training A first then building upon the A-model with the B entity to create an AB-model.
What am I doing wrong? Do I need to just use the en_core_web_lg to pre annotate and then add my own entities and train a model from scratch?