diff --git a/fastNLP/core/tester.py b/fastNLP/core/tester.py
index e4d67261..47959fd2 100644
--- a/fastNLP/core/tester.py
+++ b/fastNLP/core/tester.py
@@ -180,12 +180,11 @@ class Tester(object):
                                             f"`dict`, got {type(eval_result)}")
                         metric_name = metric.get_metric_name()
                         eval_results[metric_name] = eval_result
-
+                    pbar.close()
                     end_time = time.time()
                     test_str = f'Evaluate data in {round(end_time - start_time, 2)} seconds!'
                     # pbar.write(test_str)
                     self.logger.info(test_str)
-                    pbar.close()
         except _CheckError as e:
             prev_func_signature = _get_func_signature(self._predict_func)
             _check_loss_evaluate(prev_func_signature=prev_func_signature, func_signature=e.func_signature,
diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py
index 783997a7..787ea313 100644
--- a/fastNLP/core/trainer.py
+++ b/fastNLP/core/trainer.py
@@ -336,7 +336,7 @@ except:
 import warnings
 
 from .batch import DataSetIter, BatchIter
-from .callback import CallbackManager, CallbackException
+from .callback import CallbackManager, CallbackException, Callback
 from .dataset import DataSet
 from .losses import _prepare_losser
 from .metrics import _prepare_metrics
@@ -422,13 +422,8 @@ class Trainer(object):
                  batch_size=32, sampler=None, drop_last=False, update_every=1,
                  num_workers=0, n_epochs=10, print_every=5,
                  dev_data=None, metrics=None, metric_key=None,
-                 validate_every=-1, save_path=None, use_tqdm=True, device=None, prefetch=False,
+                 validate_every=-1, save_path=None, use_tqdm=True, device=None,
                  callbacks=None, check_code_level=0, **kwargs):
-        if prefetch and num_workers==0:
-            num_workers = 1
-        if prefetch:
-            warnings.warn("prefetch is deprecated, will be removed in version 0.5.0, please use num_workers instead.")
-
         super(Trainer, self).__init__()
         if not isinstance(model, nn.Module):
             raise TypeError(f"The type of model must be torch.nn.Module, got {type(model)}.")
@@ -566,6 +561,9 @@ class Trainer(object):
         self.step = 0
         self.start_time = None  # start timestamp
 
+        if isinstance(callbacks, Callback):
+            callbacks = [callbacks]
+
         self.callback_manager = CallbackManager(env={"trainer": self},
                                                 callbacks=callbacks)
 
@@ -617,8 +615,8 @@ class Trainer(object):
 
             if self.dev_data is not None and self.best_dev_perf is not None:
                 self.logger.info(
-                    "\nIn Epoch:{}/Step:{}, got best dev performance:".format(self.best_dev_epoch, self.best_dev_step) +
-                    self.tester._format_eval_results(self.best_dev_perf), )
+                    "\nIn Epoch:{}/Step:{}, got best dev performance:".format(self.best_dev_epoch, self.best_dev_step))
+                self.logger.info(self.tester._format_eval_results(self.best_dev_perf))
                 results['best_eval'] = self.best_dev_perf
                 results['best_epoch'] = self.best_dev_epoch
                 results['best_step'] = self.best_dev_step