diff --git a/examples/training/train_ner.py b/examples/training/train_ner.py
index 8c96dc0a4..220244b93 100644
--- a/examples/training/train_ner.py
+++ b/examples/training/train_ner.py
@@ -10,6 +10,13 @@ from spacy.tagger import Tagger
 
 
 def train_ner(nlp, train_data, entity_types):
+    # Add new words to vocab.
+    for raw_text, _ in train_data:
+        doc = nlp.make_doc(raw_text)
+        for word in doc:
+            _ = nlp.vocab[word.orth]
+
+    # Train NER.
     ner = EntityRecognizer(nlp.vocab, entity_types=entity_types)
     for itn in range(5):
         random.shuffle(train_data)
@@ -20,21 +27,30 @@ def train_ner(nlp, train_data, entity_types):
     ner.model.end_training()
     return ner
 
+def save_model(ner, model_dir):
+    model_dir = pathlib.Path(model_dir)
+    if not model_dir.exists():
+        model_dir.mkdir()
+    assert model_dir.is_dir()
+
+    with (model_dir / 'config.json').open('w') as file_:
+        json.dump(ner.cfg, file_)
+    ner.model.dump(str(model_dir / 'model'))
+    if not (model_dir / 'vocab').exists():
+        (model_dir / 'vocab').mkdir()
+    ner.vocab.dump(str(model_dir / 'vocab' / 'lexemes.bin'))
+    with (model_dir / 'vocab' / 'strings.json').open('w', encoding='utf8') as file_:
+        ner.vocab.strings.dump(file_)
+
 
 def main(model_dir=None):
-    if model_dir is not None:
-        model_dir = pathlib.Path(model_dir)
-        if not model_dir.exists():
-            model_dir.mkdir()
-        assert model_dir.is_dir()
-
     nlp = spacy.load('en', parser=False, entity=False, add_vectors=False)
 
     # v1.1.2 onwards
     if nlp.tagger is None:
         print('---- WARNING ----')
         print('Data directory not found')
-        print('please run: `python -m spacy.en.download –force all` for better performance')
+        print('please run: `python -m spacy.en.download --force all` for better performance')
         print('Using feature templates for tagging')
         print('-----------------')
         nlp.tagger = Tagger(nlp.vocab, features=Tagger.feature_templates)
@@ -56,16 +72,17 @@ def main(model_dir=None):
     nlp.tagger(doc)
     ner(doc)
     for word in doc:
-        print(word.text, word.tag_, word.ent_type_, word.ent_iob)
+        print(word.text, word.orth, word.lower, word.tag_, word.ent_type_, word.ent_iob)
 
     if model_dir is not None:
-        with (model_dir / 'config.json').open('w') as file_:
-            json.dump(ner.cfg, file_)
-        ner.model.dump(str(model_dir / 'model'))
+        save_model(ner, model_dir)
+
+
+
 
 
 if __name__ == '__main__':
-    main()
+    main('ner')
     # Who "" 2
     # is "" 2
     # Shaka "" PERSON 3
diff --git a/examples/training/train_tagger.py b/examples/training/train_tagger.py
index 6d8f66630..d5a519942 100644
--- a/examples/training/train_tagger.py
+++ b/examples/training/train_tagger.py
@@ -69,7 +69,7 @@ def main(output_dir=None):
         print(word.text, word.tag_, word.pos_)
     if output_dir is not None:
         tagger.model.dump(str(output_dir / 'pos' / 'model'))
-        with (output_dir / 'vocab' / 'strings.json').open('wb') as file_:
+        with (output_dir / 'vocab' / 'strings.json').open('w') as file_:
             tagger.vocab.strings.dump(file_)