diff --git a/custom_word_mapping.txt b/custom_word_mapping.txt
new file mode 100644
index 0000000..8ca0ff0
--- /dev/null
+++ b/custom_word_mapping.txt
@@ -0,0 +1,24011 @@
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+bloodier 24007
+intractable 24008
+cedes 24009
+politicization 24010
+relatable 24011
diff --git a/preprocess.py b/preprocess.py
index 3debf1a..f5a9156 100644
--- a/preprocess.py
+++ b/preprocess.py
@@ -10,23 +10,29 @@ def load_bin_vec(fname, vocab):
     Loads 300x1 word vecs from Google (Mikolov) word2vec
     """
     word_vecs = {}
+    print(len(vocab))
     with open(fname, "rb") as f:
         header = f.readline()
-        vocab_size, layer1_size = map(int, header.split())
+        vocab_size, layer1_size = list(map(int, header.split()))
         binary_len = np.dtype('float32').itemsize * layer1_size
-        for line in xrange(vocab_size):
+        i = 0
+        for line in range(vocab_size):
             word = []
             while True:
-                ch = f.read(1)
+                try:
+                  ch = f.read(1).decode("utf-8")
+                except:
+                  continue
                 if ch == ' ':
                     word = ''.join(word)
                     break
                 if ch != '\n':
                     word.append(ch)   
             if word in vocab:
-               word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')  
+              word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')  
             else:
-                f.read(binary_len)
+              f.read(binary_len)
+    print("Load word2vec Done")
     return word_vecs
 
 def line_to_words(line, dataset):
@@ -47,8 +53,14 @@ def get_vocab(file_list, dataset=''):
 
   for filename in file_list:
     f = open(filename, "r")
+    idx_start = 0
     for line in f:
-        words = line_to_words(line, dataset)
+        idx_start +=1
+        if idx_start == 1:
+          continue
+        actuall_line = ""
+        actuall_line = line.strip()[10:-1]
+        words = line_to_words(actuall_line, dataset)
         max_sent_len = max(max_sent_len, len(words))
         for word in words:
             if not word in word_to_idx:
@@ -96,9 +108,16 @@ def load_data(dataset, train_name, test_name='', dev_name='', padding=4):
     data_label.append(dev_label)
 
   for d, lbl, f in zip(data, data_label, files):
+    idx = 0
     for line in f:
-      words = line_to_words(line, dataset)
-      y = int(line.strip().split()[0]) + 1
+      idx +=1
+      if idx == 1:
+        continue
+      actuall_line = ""
+      actuall_line = line.strip()[10:-1]
+      words = line_to_words(actuall_line, dataset)
+      # y = int(line.strip().split()[0]) + 1
+      y = int(line.strip()[7])
       sent = [word_to_idx[word] for word in words]
       # end padding
       if len(sent) < max_sent_len + padding:
@@ -114,7 +133,7 @@ def load_data(dataset, train_name, test_name='', dev_name='', padding=4):
     f_test.close()
   if not dev_name == '':
     f_dev.close()
-
+  print("Load Data Done")
   return word_to_idx, np.array(train, dtype=np.int32), np.array(train_label, dtype=np.int32), np.array(test, dtype=np.int32), np.array(test_label, dtype=np.int32), np.array(dev, dtype=np.int32), np.array(dev_label, dtype=np.int32)
 
 def clean_str(string):
@@ -187,22 +206,22 @@ def main():
   # Write word mapping to text file.
   with open(dataset + '_word_mapping.txt', 'w+') as embeddings_f:
     embeddings_f.write("*PADDING* 1\n")
-    for word, idx in sorted(word_to_idx.items(), key=operator.itemgetter(1)):
+    for word, idx in sorted(list(word_to_idx.items()), key=operator.itemgetter(1)):
       embeddings_f.write("%s %d\n" % (word, idx))
 
   # Load word2vec
   w2v = load_bin_vec(args.w2v, word_to_idx)
   V = len(word_to_idx) + 1
-  print 'Vocab size:', V
+  print('Vocab size:', V)
 
   # Not all words in word_to_idx are in w2v.
   # Word embeddings initialized to random Unif(-0.25, 0.25)
-  embed = np.random.uniform(-0.25, 0.25, (V, len(w2v.values()[0])))
+  embed = np.random.uniform(-0.25, 0.25, (V, len(list(w2v.values())[0])))
   embed[0] = 0
-  for word, vec in w2v.items():
+  for word, vec in list(w2v.items()):
     embed[word_to_idx[word] - 1] = vec
 
-  print 'train size:', train.shape
+  print('train size:', train.shape)
 
   filename = dataset + '.hdf5'
   with h5py.File(filename, "w") as f: