diff --git a/convert_tf_checkpoint_to_pytorch.py b/convert_tf_checkpoint_to_pytorch.py
index d4d47a3bd6..dfcdbee42d 100755
--- a/convert_tf_checkpoint_to_pytorch.py
+++ b/convert_tf_checkpoint_to_pytorch.py
@@ -26,14 +26,35 @@ import numpy as np
 
 from modeling import BertConfig, BertModel
 
+parser = argparse.ArgumentParser()
 
-def convert(config_path, ckpt_path, out_path=None):
+## Required parameters
+parser.add_argument("--tf_checkpoint_path",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "Path the TensorFlow checkpoint path.")
+parser.add_argument("--bert_config_file",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "The config json file corresponding to the pre-trained BERT model. \n"
+                        "This specifies the model architecture.")
+parser.add_argument("--pytorch_dump_path",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "Path to the output PyTorch model.")
+
+args = parser.parse_args()
+
+def convert():
     # Initialise PyTorch model
-    config = BertConfig.from_json_file(config_path)
+    config = BertConfig.from_json_file(args.bert_config_file)
     model = BertModel(config)
 
     # Load weights from TF model
-    path = ckpt_path
+    path = args.tf_checkpoint_path
     print("Converting TensorFlow checkpoint from {}".format(path))
 
     init_vars = tf.train.list_variables(path)
@@ -47,17 +68,11 @@ def convert(config_path, ckpt_path, out_path=None):
         arrays.append(array)
 
     for name, array in zip(names, arrays):
-        if not name.startswith("bert"):
-            print("Skipping {}".format(name))
-            continue
-        else:
-            name = name.replace("bert/", "")  # skip "bert/"
+        name = name[5:]  # skip "bert/"
         print("Loading {}".format(name))
         name = name.split('/')
-        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
-        # which are not required for using pretrained model
-        if name[0] in ['redictions', 'eq_relationship'] or name[-1] == "adam_v" or  name[-1] == "adam_m":
-            print("Skipping {}".format("/".join(name)))
+        if name[0] in ['redictions', 'eq_relationship']:
+            print("Skipping")
             continue
         pointer = model
         for m_name in name:
@@ -84,32 +99,7 @@ def convert(config_path, ckpt_path, out_path=None):
         pointer.data = torch.from_numpy(array)
 
     # Save pytorch-model
-    if out_path is not None:
-        torch.save(model.state_dict(), out_path)
-    return model
-
+    torch.save(model.state_dict(), args.pytorch_dump_path)
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    ## Required parameters
-    parser.add_argument("--tf_checkpoint_path",
-                        default=None,
-                        type=str,
-                        required=True,
-                        help="Path the TensorFlow checkpoint path.")
-    parser.add_argument("--bert_config_file",
-                        default=None,
-                        type=str,
-                        required=True,
-                        help="The config json file corresponding to the pre-trained BERT model. \n"
-                             "This specifies the model architecture.")
-    parser.add_argument("--pytorch_dump_path",
-                        default=None,
-                        type=str,
-                        required=False,
-                        help="Path to the output PyTorch model.")
-
-    args = parser.parse_args()
-    print(args)
-    convert(args.bert_config_file, args.tf_checkpoint_path, args.pytorch_dump_path)
+    convert()