diff --git a/examples/training/conllu.py b/examples/training/conllu.py
index 3bb6248af..7f8c817d2 100644
--- a/examples/training/conllu.py
+++ b/examples/training/conllu.py
@@ -30,6 +30,7 @@ random.seed(0)
 numpy.random.seed(0)
 
 def minibatch_by_words(items, size=5000):
+    random.shuffle(items)
     if isinstance(size, int):
         size_ = itertools.repeat(size)
     else:
@@ -39,10 +40,17 @@ def minibatch_by_words(items, size=5000):
         batch_size = next(size_)
         batch = []
         while batch_size >= 0:
-            doc, gold = next(items)
+            try:
+                doc, gold = next(items)
+            except StopIteration:
+                yield batch
+                return
             batch_size -= len(doc)
             batch.append((doc, gold))
-        yield batch
+        if batch:
+            yield batch
+        else:
+            break
 
 ################
 # Data reading #
@@ -146,10 +154,6 @@ def _make_gold(nlp, text, sent_annots):
     doc = nlp.make_doc(text)
     flat.pop('spaces')
     gold = GoldParse(doc, **flat)
-    #for annot in gold.orig_annot:
-    #    print(annot)
-    #for i in range(len(doc)):
-    #    print(doc[i].text, gold.words[i], gold.labels[i], gold.heads[i])
     return doc, gold
 
 #############################
@@ -168,12 +172,6 @@ def golds_to_gold_tuples(docs, golds):
     return tuples
 
 
-def refresh_docs(docs):
-    vocab = docs[0].vocab
-    return [Doc(vocab, words=[t.text for t in doc],
-                       spaces=[t.whitespace_ for t in doc])
-            for doc in docs]
-
 ##############
 # Evaluation #
 ##############
@@ -357,25 +355,24 @@ class TreebankPaths(object):
 )
 def main(ud_dir, corpus, config, parses_loc='/tmp/dev.conllu', limit=10):
     paths = TreebankPaths(ud_dir, corpus)
+    print("Train and evaluate", corpus, "using lang", paths.lang)
     nlp = load_nlp(paths.lang, config)
 
     docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
-                            limit=limit)
+                            max_doc_length=config.max_doc_length, limit=limit)
 
     optimizer = initialize_pipeline(nlp, docs, golds, config)
-    n_train_words = sum(len(doc) for doc in docs)
-    print("Begin training (%d words)" % n_train_words)
     for i in range(config.nr_epoch):
-        docs = refresh_docs(docs)
+        docs = [nlp.make_doc(doc.text) for doc in docs]
         batches = minibatch_by_words(list(zip(docs, golds)), size=config.batch_size)
         losses = {}
-        for batch in tqdm.tqdm(batches, total=n_train_words//config.batch_size):
-            if not batch:
-                continue
-            batch_docs, batch_gold = zip(*batch)
-
-            nlp.update(batch_docs, batch_gold, sgd=optimizer,
-                        drop=config.dropout, losses=losses)
+        n_train_words = sum(len(doc) for doc in docs)
+        with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
+            for batch in batches:
+                batch_docs, batch_gold = zip(*batch)
+                pbar.update(sum(len(doc) for doc in batch_docs))
+                nlp.update(batch_docs, batch_gold, sgd=optimizer,
+                           drop=config.dropout, losses=losses)
         
         with nlp.use_params(optimizer.averages):
             dev_docs, scorer = parse_dev_data(nlp, paths.dev.text, paths.dev.conllu)