Allow one to use the pretrained model in evaluation when do_train is not selected

examples/run_classifier.py

@@ -430,8 +430,8 @@ def main():
 
     if not args.do_train and not args.do_eval:
         raise ValueError("At least one of `do_train` or `do_eval` must be True.")
-
-    if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
+        
+    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
         raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
     os.makedirs(args.output_dir, exist_ok=True)
 
@@ -554,7 +554,8 @@ def main():
     # Save a trained 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")
-    torch.save(model_to_save.state_dict(), output_model_file)
+    if args.do_train:
+        torch.save(model_to_save.state_dict(), output_model_file)
 
     # Load a trained model that you have fine-tuned
     model_state_dict = torch.load(output_model_file)