diff --git a/neural_compressor/mix_precision.py b/neural_compressor/mix_precision.py
index 1f5bc0445ba..f997e9f5471 100644
--- a/neural_compressor/mix_precision.py
+++ b/neural_compressor/mix_precision.py
@@ -252,35 +252,47 @@ def metric(self):
     def metric(self, user_metric):
         """Set metric class or a dict of built-in metric configures.
 
-        1. neural_compressor have many built-in metrics, user can pass a metric configure dict to tell neural 
-           compressor what metric will be use.
-           You can set multi-metrics to evaluate the performance of a specific model.
+        1. neural_compressor have many built-in metrics,
+           user can pass a metric configure dict to tell neural compressor what metric will be use.
+           You also can set multi-metrics to evaluate the performance of a specific model.
                 Single metric:
                     {topk: 1}
-
                 Multi-metrics:
                     {topk: 1,
                      MSE: {compare_label: False},
                      weight: [0.5, 0.5],
                      higher_is_better: [True, False]
                     }
-            Refer to this [file](../docs/source/metric.md#supported-built-in-metric-matrix) for built-in metric list
-        2. User also can set specific metric through this api. The metric class should take the outputs of the model or
-           postprocess(if have) as inputs, neural_compressor built-in metric always take(predictions, labels) as inputs
-           for update, and user_metric.metric_cls should be sub_class of neural_compressor.metric.BaseMetric.
+        For the built-in metrics, please refer to below link:
+        https://github.com/intel/neural-compressor/blob/master/docs/source/metric.md#supported-built-in-metric-matrix.
+
+        2. User also can get the built-in metrics by neural_compressor.Metric:
+            Metric(name="topk", k=1)
+        3. User also can set specific metric through this api. The metric class should take the outputs of the model or
+           postprocess(if have) as inputs, neural_compressor built-in metric always take(predictions, labels)
+           as inputs for update, and user_metric.metric_cls should be sub_class of neural_compressor.metric.BaseMetric.
 
         Args:
-            user_metric(neural_compressor.metric.Metric or a dict of built-in metric configures):
+            user_metric(neural_compressor.metric.Metric or a dict of built-in metric configurations):
                 The object of Metric or a dict of built-in metric configurations.
+
         """
-        from .metric import Metric as NCMetric, METRICS
+        from .metric import Metric as NCMetric
+        from .metric import METRICS
         if isinstance(user_metric, dict):
             metric_cfg = user_metric
         else:
             if isinstance(user_metric, NCMetric):
-                name = user_metric.name
-                metric_cls = user_metric.metric_cls
-                metric_cfg = {name: {**user_metric.kwargs}}
+                if user_metric.metric_cls is None:
+                    name = user_metric.name
+                    metric_cls = METRICS(self.conf.mixed_precision.framework).metrics[name]
+                    metric_cfg = {name: {**user_metric.kwargs}}
+                    self._metric = metric_cfg
+                    return
+                else:
+                    name = user_metric.name
+                    metric_cls = user_metric.metric_cls
+                    metric_cfg = {name: {**user_metric.kwargs}}
             else:
                 for i in ['reset', 'update', 'result']:
                     assert hasattr(user_metric, i), 'Please realise {} function' \