diff --git a/bin/parser/train.py b/bin/parser/train.py
index a89316ef1..998c74819 100755
--- a/bin/parser/train.py
+++ b/bin/parser/train.py
@@ -21,7 +21,7 @@ from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
 from spacy.syntax.parser import GreedyParser
 from spacy.syntax.parser import OracleError
 from spacy.syntax.util import Config
-from spacy.syntax.conll import GoldParse
+from spacy.syntax.conll import GoldParse, is_punct_label
 
 
 def is_punct_label(label):
@@ -206,15 +206,22 @@ def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
         heads_corr = 0
         pos_corr = 0
         n_tokens = 0
+        n_all_tokens = 0
         for gold_sent in gold_sents:
-            tokens = nlp.tokenizer(gold_sent.raw_text)
-            gold_sent.align_to_tokens(tokens, nlp.parser.moves.label_ids)
+            if gold_preproc:
+                #print ' '.join(gold_sent.words)
+                tokens = nlp.tokenizer.tokens_from_list(gold_sent.words)
+                gold_sent.map_heads(nlp.parser.moves.label_ids)
+            else:
+                tokens = nlp.tokenizer(gold_sent.raw_text)
+                gold_sent.align_to_tokens(tokens, nlp.parser.moves.label_ids)
             nlp.tagger(tokens)
             heads_corr += nlp.parser.train(tokens, gold_sent, force_gold=force_gold)
             pos_corr += nlp.tagger.train(tokens, gold_sent.tags)
-            n_tokens += len(tokens)
+            n_tokens += gold_sent.n_non_punct
+            n_all_tokens += len(tokens)
         acc = float(heads_corr) / n_tokens
-        pos_acc = float(pos_corr) / n_tokens
+        pos_acc = float(pos_corr) / n_all_tokens
         print '%d: ' % itn, '%.3f' % acc, '%.3f' % pos_acc
         random.shuffle(gold_sents)
     nlp.parser.model.end_training()
@@ -241,21 +248,26 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
         nlp.tagger(tokens)
         nlp.parser(tokens)
         for i, token in enumerate(tokens):
-            pos_corr += token.tag_ == tag_strs[i]
+            pos_corr += token.tag_ == gold_sent.tags[i]
             n_tokens += 1
-            if heads[i] is None:
+            if gold_sent.heads[i] is None:
                 skipped += 1
                 continue
-            if is_punct_label(labels[i]):
-                continue
-            uas_corr += token.head.i == heads[i]
-            las_corr += token.head.i == heads[i] and token.dep_ == labels[i]
-            #print token.orth_, token.head.orth_, token.dep_, labels[i]
-            total += 1
+            #print i, token.orth_, token.head.i, gold_sent.py_heads[i], gold_sent.labels[i],
+            #print gold_sent.is_correct(i, token.head.i)
+            if gold_sent.labels[i] != 'P':
+                n_corr += gold_sent.is_correct(i, token.head.i)
+                total += 1
     print loss, skipped, (loss+skipped + total)
     print pos_corr / n_tokens
-    print float(las_corr) / (total + loss)
-    return float(uas_corr) / (total + loss)
+    return float(n_corr) / (total + loss)
+
+
+def read_gold(loc, n=0):
+    sent_strs = open(loc).read().strip().split('\n\n')
+    if n == 0:
+        n = len(sent_strs)
+    return [GoldParse.from_docparse(sent) for sent in sent_strs[:n]]
 
 
 @plac.annotations(
@@ -265,8 +277,8 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
     n_sents=("Number of training sentences", "option", "n", int)
 )
 def main(train_loc, dev_loc, model_dir, n_sents=0):
-    train(English, read_gold(train_loc, n=n_sents), model_dir,
-          gold_preproc=False, force_gold=False)
+    #train(English, read_gold(train_loc, n=n_sents), model_dir,
+    #      gold_preproc=False, force_gold=False)
     print evaluate(English, read_gold(dev_loc), model_dir, gold_preproc=False)