diff --git a/fastNLP/core/controllers/evaluator.py b/fastNLP/core/controllers/evaluator.py
index 479686e1..2e3678d3 100644
--- a/fastNLP/core/controllers/evaluator.py
+++ b/fastNLP/core/controllers/evaluator.py
@@ -364,16 +364,16 @@ class _MetricsWrapper:
             else:
                 args.append(batch)
             if not isinstance(outputs, dict):
-                raise RuntimeError(f"The output of your model is of type:`{type(batch)}`, please either directly"
+                raise RuntimeError(f"The output of your model is of type:`{type(outputs)}`, please either directly"
                                    f" return a dict from your model or use `output_mapping` to convert it into dict type.")
             if isinstance(metric, Metric):
-                auto_param_call(metric.update, batch, *args)
+                auto_param_call(metric.update, outputs, *args)
             elif _is_torchmetrics_metric(metric):
-                auto_param_call(metric.update, batch, *args)
+                auto_param_call(metric.update, outputs, *args)
             elif _is_allennlp_metric(metric):
-                auto_param_call(metric.__call__, batch, *args)
+                auto_param_call(metric.__call__, outputs, *args)
             elif _is_paddle_metric(metric):
-                res = auto_param_call(metric.compute, batch, *args)
+                res = auto_param_call(metric.compute, outputs, *args)
                 metric.update(res)
 
     def reset(self):