diff --git a/bin/parser/train_ud.py b/bin/parser/train_ud.py
index c96faf7b9..4b3080ce5 100644
--- a/bin/parser/train_ud.py
+++ b/bin/parser/train_ud.py
@@ -24,9 +24,10 @@ import io
 
 
 
-def read_conllx(loc):
+def read_conllx(loc, n=0):
     with io.open(loc, 'r', encoding='utf8') as file_:
         text = file_.read()
+    i = 0
     for sent in text.strip().split('\n\n'):
         lines = sent.strip().split('\n')
         if lines:
@@ -47,6 +48,9 @@ def read_conllx(loc):
                     raise
             tuples = [list(t) for t in zip(*tokens)]
             yield (None, [[tuples, []]])
+            i += 1
+            if n >= 1 and i >= n:
+                break
 
 
 def score_model(vocab, tagger, parser, gold_docs, verbose=False):
@@ -73,7 +77,7 @@ def main(train_loc, dev_loc, model_dir, tag_map_loc=None):
 
     actions = ArcEager.get_actions(gold_parses=train_sents)
     features = get_templates('basic')
-    
+
     model_dir = pathlib.Path(model_dir)
     if not (model_dir / 'deps').exists():
         (model_dir / 'deps').mkdir()
@@ -95,25 +99,26 @@ def main(train_loc, dev_loc, model_dir, tag_map_loc=None):
                 for tag in tags:
                     assert tag in tag_map, repr(tag)
     tagger = Tagger(vocab, tag_map=tag_map)
-    parser = DependencyParser(vocab, actions=actions, features=features)
-    
+    parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0)
+
     for itn in range(15):
+        loss = 0.
         for _, doc_sents in train_sents:
             for (ids, words, tags, heads, deps, ner), _ in doc_sents:
                 doc = Doc(vocab, words=words)
                 gold = GoldParse(doc, tags=tags, heads=heads, deps=deps)
                 tagger(doc)
-                parser.update(doc, gold)
+                loss += parser.update(doc, gold, itn=itn)
                 doc = Doc(vocab, words=words)
                 tagger.update(doc, gold)
         random.shuffle(train_sents)
         scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
-        print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc))
+        print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc))
     nlp = Language(vocab=vocab, tagger=tagger, parser=parser)
     nlp.end_training(model_dir)
     scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
     print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
- 
+
 
 if __name__ == '__main__':
     plac.call(main)