fix max_steps of msra_ner. (#1451)

Author: ZHUI

examples/information_extraction/msra_ner/train.py

@@ -42,7 +42,7 @@
 parser = argparse.ArgumentParser()
 
 # yapf: disable
-parser.add_argument("--model_type", default="bert", type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), )
+parser.add_argument("--model_type", default="bert", type=str, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), )
 parser.add_argument("--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join( sum([ list(classes[-1].pretrained_init_configuration.keys())  for classes in MODEL_CLASSES.values() ], [])), )
 parser.add_argument("--dataset", default="msra_ner", type=str, choices=["msra_ner", "peoples_daily_ner"] ,help="The named entity recognition datasets.")
 parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.")
@@ -218,7 +218,7 @@ def do_train(args):
             optimizer.step()
             lr_scheduler.step()
             optimizer.clear_grad()
-            if global_step % args.save_steps == 0 or global_step == last_step:
+            if global_step % args.save_steps == 0 or global_step == num_training_steps:
                 if paddle.distributed.get_rank() == 0:
                     if args.dataset == "peoples_daily_ner":
                         evaluate(model, loss_fct, metric, dev_data_loader,
@@ -229,6 +229,8 @@ def do_train(args):
                     paddle.save(model.state_dict(),
                                 os.path.join(args.output_dir,
                                              "model_%d.pdparams" % global_step))
+            if global_step >= num_training_steps:
+                return
 
 
 if __name__ == "__main__":