Don't call begin_training if updating new model (see #3059) [ci skip]

examples/training/train_ner.py

@@ -56,7 +56,10 @@ def main(model=None, output_dir=None, n_iter=100):
     # 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()
+        # reset and initialize the weights randomly – but only if we're
+        # training a new model
+        if model is None:
+            nlp.begin_training()
         for itn in range(n_iter):
             random.shuffle(TRAIN_DATA)
             losses = {}
@@ -68,7 +71,6 @@ def main(model=None, output_dir=None, n_iter=100):
                     texts,  # batch of texts
                     annotations,  # batch of annotations
                     drop=0.5,  # dropout - make it harder to memorise data
-                    sgd=optimizer,  # callable to update weights
                     losses=losses,
                 )
             print("Losses", losses)