diff --git a/examples/training/train_new_entity_type.py b/examples/training/train_new_entity_type.py
index a0edde45c..7e1b19c0e 100644
--- a/examples/training/train_new_entity_type.py
+++ b/examples/training/train_new_entity_type.py
@@ -81,7 +81,6 @@ def main(model=None, new_model_name='animal', output_dir=None, n_iter=20):
     else:
         nlp = spacy.blank('en')  # create blank Language class
         print("Created blank 'en' model")
-
     # Add entity recognizer to model if it's not in the pipeline
     # nlp.create_pipe works for built-ins that are registered with spaCy
     if 'ner' not in nlp.pipe_names:
@@ -92,11 +91,18 @@ def main(model=None, new_model_name='animal', output_dir=None, n_iter=20):
         ner = nlp.get_pipe('ner')
 
     ner.add_label(LABEL)   # add new entity label to entity recognizer
+    if model is None:
+        optimizer = nlp.begin_training()
+    else:
+        # Note that 'begin_training' initializes the models, so it'll zero out
+        # existing entity types.
+        optimizer = nlp.entity.create_optimizer()
+
+
 
     # get names of other pipes to disable them during training
     other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
     with nlp.disable_pipes(*other_pipes):  # only train NER
-        optimizer = nlp.begin_training()
         for itn in range(n_iter):
             random.shuffle(TRAIN_DATA)
             losses = {}