diff --git a/tf-ner-poc/src/main/python/namefinder/namefinder.py b/tf-ner-poc/src/main/python/namefinder/namefinder.py
index 00560ac..9150bd1 100644
--- a/tf-ner-poc/src/main/python/namefinder/namefinder.py
+++ b/tf-ner-poc/src/main/python/namefinder/namefinder.py
@@ -62,28 +62,6 @@
     def __init__(self, vector_size=100):
         self.__vector_size = vector_size
 
-    def load_glove(self, glove_file):
-        with open(glove_file) as f:
-
-            word_dict = {}
-            embeddings = []
-
-            for line in f:
-                parts = line.strip().split(" ")
-                if len(parts) != self.__vector_size + 1:
-                    #print("Bad Vector: ",len(line),len(parts), line)
-                    raise VectorException("Bad Vector in line: {}, size: {} vector: {}".format(len(line),len(parts), line))
-                    continue
-                word_dict[parts[0]] = len(word_dict)
-                embeddings.append(np.array(parts[1:], dtype=np.float32))
-
-        # Create a reverse word dict
-        rev_word_dict = {}
-        for word, id in word_dict.items():
-            rev_word_dict[id] = word
-
-        return word_dict, rev_word_dict, np.asarray(embeddings)
-
     def load_data(self, word_dict, file):
         with open(file) as f:
             raw_data = f.readlines()
@@ -344,15 +322,47 @@
         for i, tag in enumerate(tags):
             f.write('{}\n'.format(tag))
 
+def load_glove(glove_file):
+    with open(glove_file) as f:
+
+        word_dict = {}
+        embeddings = []
+
+        vector_size = -1
+
+        for line in f:
+            parts = line.strip().split(" ")
+
+            if vector_size == -1:
+                if len(parts) == 2:
+                    vector_size = int(parts[1])
+                    continue
+                vector_size = len(parts) - 1
+
+            if len(parts) != vector_size + 1:
+                #print("Bad Vector: ",len(line),len(parts), line)
+                raise VectorException("Bad Vector in line: {}, size: {} vector: {}".format(len(line),len(parts), line))
+                continue
+            word_dict[parts[0]] = len(word_dict)
+            embeddings.append(np.array(parts[1:], dtype=np.float32))
+
+    # Create a reverse word dict
+    rev_word_dict = {}
+    for word, id in word_dict.items():
+        rev_word_dict[id] = word
+
+    return word_dict, rev_word_dict, np.asarray(embeddings), vector_size
+
 def main():
 
     if len(sys.argv) != 5:
         print("Usage namefinder.py embedding_file train_file dev_file test_file")
         return
 
-    name_finder = NameFinder(100)
+    word_dict, rev_word_dict, embeddings, vector_size = load_glove(sys.argv[1])
 
-    word_dict, rev_word_dict, embeddings = name_finder.load_glove(sys.argv[1])
+    name_finder = NameFinder(vector_size)
+
     sentences, labels, char_set = name_finder.load_data(word_dict, sys.argv[2])
     sentences_dev, labels_dev, char_set_dev = name_finder.load_data(word_dict, sys.argv[3])