Fix begin_training if get_gold_tuples is None

examples/training/train_intent_parser.py

@@ -94,7 +94,7 @@ def main(model=None, output_dir=None, n_iter=100):
 
     other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
     with nlp.disable_pipes(*other_pipes):  # only train parser
-        optimizer = nlp.begin_training(lambda: [])
+        optimizer = nlp.begin_training()
         for itn in range(n_iter):
             random.shuffle(TRAIN_DATA)
             losses = {}

examples/training/train_new_entity_type.py

@@ -87,7 +87,7 @@ def main(model=None, new_model_name='animal', output_dir=None, n_iter=50):
     other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
     with nlp.disable_pipes(*other_pipes):  # only train NER
         random.seed(0)
-        optimizer = nlp.begin_training(lambda: [])
+        optimizer = nlp.begin_training()
         for itn in range(n_iter):
             losses = {}
             gold_parses = get_gold_parses(nlp.make_doc, TRAIN_DATA)

examples/training/train_parser.py

@@ -64,7 +64,7 @@ def main(model=None, output_dir=None, n_iter=1000):
     # get names of other pipes to disable them during training
     other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'parser']
     with nlp.disable_pipes(*other_pipes):  # only train parser
-        optimizer = nlp.begin_training(lambda: [])
+        optimizer = nlp.begin_training()
         for itn in range(n_iter):
             random.shuffle(TRAIN_DATA)
             losses = {}

examples/training/train_tagger.py

@@ -61,7 +61,7 @@ def main(lang='en', output_dir=None, n_iter=25):
     tagger = nlp.create_pipe('tagger')
     nlp.add_pipe(tagger)
 
-    optimizer = nlp.begin_training(lambda: [])
+    optimizer = nlp.begin_training()
     for i in range(n_iter):
         random.shuffle(TRAIN_DATA)
         losses = {}

examples/training/train_textcat.py

@@ -59,7 +59,7 @@ def main(model=None, output_dir=None, n_iter=20):
     # get names of other pipes to disable them during training
     other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat']
     with nlp.disable_pipes(*other_pipes):  # only train textcat
-        optimizer = nlp.begin_training(lambda: [])
+        optimizer = nlp.begin_training()
         print("Training the model...")
         print('{:^5}\t{:^5}\t{:^5}\t{:^5}'.format('LOSS', 'P', 'R', 'F'))
         for i in range(n_iter):

spacy/language.py

@@ -436,8 +436,10 @@ class Language(object):
         **cfg: Config parameters.
         RETURNS: An optimizer
         """
+        if get_gold_tuples is None:
+            get_gold_tuples = lambda: []
         # Populate vocab
-        if get_gold_tuples is not None:
+        else:
             for _, annots_brackets in get_gold_tuples():
                 for annots, _ in annots_brackets:
                     for word in annots[1]: