add do_lower_case arg and adjust model saving for lm finetuning.

examples/run_lm_finetuning.py

@@ -461,6 +461,9 @@ def main():
     parser.add_argument("--on_memory",
                         action='store_true',
                         help="Whether to load train samples into memory or use disk")
+    parser.add_argument("--do_lower_case",
+                        action='store_true',
+                        help="Whether to lower case the input text. True for uncased models, False for cased models.")
     parser.add_argument("--local_rank",
                         type=int,
                         default=-1,
@@ -612,12 +615,12 @@ def main():
                     optimizer.zero_grad()
                     global_step += 1
 
+        # Save a trained model
         logger.info("** ** * Saving fine - tuned model ** ** * ")
+        model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
         output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
-        if n_gpu > 1:
-            torch.save(model.module.bert.state_dict(), output_model_file)
-        else:
-            torch.save(model.bert.state_dict(), output_model_file)
+        if args.do_train:
+            torch.save(model_to_save.state_dict(), output_model_file)
 
 
 def _truncate_seq_pair(tokens_a, tokens_b, max_length):