diff --git a/examples/training/train_ner.py b/examples/training/train_ner.py
index 7e08f23d5..8c96dc0a4 100644
--- a/examples/training/train_ner.py
+++ b/examples/training/train_ner.py
@@ -6,6 +6,7 @@ import random
 import spacy
 from spacy.pipeline import EntityRecognizer
 from spacy.gold import GoldParse
+from spacy.tagger import Tagger
 
 
 def train_ner(nlp, train_data, entity_types):
@@ -29,6 +30,15 @@ def main(model_dir=None):
 
     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('Using feature templates for tagging')
+        print('-----------------')
+        nlp.tagger = Tagger(nlp.vocab, features=Tagger.feature_templates)
+
     train_data = [
         (
             'Who is Shaka Khan?',
diff --git a/examples/training/train_tagger.py b/examples/training/train_tagger.py
index 9fac234dc..6d8f66630 100644
--- a/examples/training/train_tagger.py
+++ b/examples/training/train_tagger.py
@@ -10,8 +10,9 @@ from pathlib import Path
 from spacy.vocab import Vocab
 from spacy.tagger import Tagger
 from spacy.tokens import Doc
-import random
+from spacy.gold import GoldParse
 
+import random
 
 # You need to define a mapping from your data's part-of-speech tag names to the
 # Universal Part-of-Speech tag set, as spaCy includes an enum of these tags.
@@ -20,24 +21,25 @@ import random
 # You may also specify morphological features for your tags, from the universal
 # scheme.
 TAG_MAP = {
-        'N': {"pos": "NOUN"},
-        'V': {"pos": "VERB"},
-        'J': {"pos": "ADJ"}
-    }
+    'N': {"pos": "NOUN"},
+    'V': {"pos": "VERB"},
+    'J': {"pos": "ADJ"}
+}
 
 # Usually you'll read this in, of course. Data formats vary.
 # Ensure your strings are unicode.
 DATA = [
     (
         ["I", "like", "green", "eggs"],
-        ["N", "V",    "J",     "N"]
+        ["N", "V", "J", "N"]
     ),
     (
         ["Eat", "blue", "ham"],
-        ["V",   "J",    "N"]
+        ["V", "J", "N"]
     )
 ]
-    
+
+
 def ensure_dir(path):
     if not path.exists():
         path.mkdir()
@@ -49,18 +51,19 @@ def main(output_dir=None):
         ensure_dir(output_dir)
         ensure_dir(output_dir / "pos")
         ensure_dir(output_dir / "vocab")
-    
+
     vocab = Vocab(tag_map=TAG_MAP)
     # The default_templates argument is where features are specified. See
     # spacy/tagger.pyx for the defaults.
     tagger = Tagger(vocab)
-    for i in range(5):
+    for i in range(25):
         for words, tags in DATA:
             doc = Doc(vocab, words=words)
-            tagger.update(doc, tags)
+            gold = GoldParse(doc, tags=tags)
+            tagger.update(doc, gold)
         random.shuffle(DATA)
     tagger.model.end_training()
-    doc = Doc(vocab, orths_and_spaces=zip(["I", "like", "blue", "eggs"], [True]*4))
+    doc = Doc(vocab, orths_and_spaces=zip(["I", "like", "blue", "eggs"], [True] * 4))
     tagger(doc)
     for word in doc:
         print(word.text, word.tag_, word.pos_)