Fixes to the TensorFlow conversion tool

examples/extract_features.py

@@ -57,7 +57,7 @@ class InputFeatures(object):
 
 
 def convert_examples_to_features(examples, seq_length, tokenizer):
-    """Loads a data file into a list of `InputBatch`s."""
+    """Loads a data file into a list of `InputFeature`s."""
 
     features = []
     for (ex_index, example) in enumerate(examples):

pytorch_pretrained_bert/modeling.py

@@ -76,7 +76,7 @@ def load_tf_weights_in_bert(model, tf_checkpoint_path):
         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 any(n in ["adam_v", "adam_m"] for n in name):
+        if any(n in ["adam_v", "adam_m", "global_step"] for n in name):
             print("Skipping {}".format("/".join(name)))
             continue
         pointer = model
@@ -92,7 +92,11 @@ def load_tf_weights_in_bert(model, tf_checkpoint_path):
             elif l[0] == 'output_weights':
                 pointer = getattr(pointer, 'weight')
             else:
-                pointer = getattr(pointer, l[0])
+                try:
+                    pointer = getattr(pointer, l[0])
+                except AttributeError:
+                    print("Skipping {}".format("/".join(name)))
+                    continue
             if len(l) >= 2:
                 num = int(l[1])
                 pointer = pointer[num]