diff --git a/examples/chainer_sentiment.py b/examples/chainer_sentiment.py
index ac3881e75..747ef508a 100644
--- a/examples/chainer_sentiment.py
+++ b/examples/chainer_sentiment.py
@@ -3,6 +3,9 @@ import plac
 import random
 import six
 
+import cProfile
+import pstats
+
 import pathlib
 import cPickle as pickle
 from itertools import izip
@@ -81,7 +84,7 @@ class SentimentModel(Chain):
     def __init__(self, nlp, shape, **settings):
         Chain.__init__(self,
             embed=_Embed(shape['nr_vector'], shape['nr_dim'], shape['nr_hidden'],
-                initialW=lambda arr: set_vectors(arr, nlp.vocab)),
+                set_vectors=lambda arr: set_vectors(arr, nlp.vocab)),
             encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
             attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
             predict=_Predict(shape['nr_hidden'], shape['nr_class']))
@@ -95,11 +98,11 @@ class SentimentModel(Chain):
 
 
 class _Embed(Chain):
-    def __init__(self, nr_vector, nr_dim, nr_out):
+    def __init__(self, nr_vector, nr_dim, nr_out, set_vectors=None):
         Chain.__init__(self,
-            embed=L.EmbedID(nr_vector, nr_dim),
+            embed=L.EmbedID(nr_vector, nr_dim, initialW=set_vectors),
             project=L.Linear(None, nr_out, nobias=True))
-        #self.embed.unchain_backward()
+        self.embed.W.volatile = False
 
     def __call__(self, sentence):
         return [self.project(self.embed(ts)) for ts in F.transpose(sentence)]
@@ -214,7 +217,6 @@ def set_vectors(vectors, vocab):
             vectors[lex.rank + 1] = lex.vector
         else:
             lex.norm = 0
-    vectors.unchain_backwards()
     return vectors
 
 
@@ -223,7 +225,9 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
         by_sentence=True):
     nlp = spacy.load('en', entity=False)
     if 'nr_vector' not in lstm_shape:
-        lstm_shape['nr_vector'] = max(lex.rank+1 for lex in vocab if lex.has_vector)
+        lstm_shape['nr_vector'] = max(lex.rank+1 for lex in nlp.vocab if lex.has_vector)
+    if 'nr_dim' not in lstm_shape:
+        lstm_shape['nr_dim'] = nlp.vocab.vectors_length
     print("Make model")
     model = Classifier(SentimentModel(nlp, lstm_shape, **lstm_settings))
     print("Parsing texts...")
@@ -240,7 +244,7 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
     optimizer = chainer.optimizers.Adam()
     optimizer.setup(model)
     updater = chainer.training.StandardUpdater(train_iter, optimizer, device=0)
-    trainer = chainer.training.Trainer(updater, (20, 'epoch'), out='result')
+    trainer = chainer.training.Trainer(updater, (1, 'epoch'), out='result')
 
     trainer.extend(extensions.Evaluator(dev_iter, model, device=0))
     trainer.extend(extensions.LogReport())
@@ -305,11 +309,14 @@ def main(model_dir, train_dir, dev_dir,
         dev_labels = xp.asarray(dev_labels, dtype='i')
         lstm = train(train_texts, train_labels, dev_texts, dev_labels,
                      {'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 2,
-                      'nr_vector': 2000, 'nr_dim': 32},
+                      'nr_vector': 5000},
                       {'dropout': 0.5, 'lr': learn_rate},
                       {},
                       nb_epoch=nb_epoch, batch_size=batch_size)
 
 
 if __name__ == '__main__':
+    #cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
+    #s = pstats.Stats("Profile.prof")
+    #s.strip_dirs().sort_stats("time").print_stats()
     plac.call(main)