diff --git a/fastNLP/core/predictor.py b/fastNLP/core/predictor.py
index ce016bb6..2d6a7380 100644
--- a/fastNLP/core/predictor.py
+++ b/fastNLP/core/predictor.py
@@ -14,12 +14,12 @@ from .utils import _build_args, _move_dict_value_to_device, _get_model_device
 
 class Predictor(object):
     """
-    An interface for predicting outputs based on trained models.
+    一个根据训练模型预测输出的预测器（Predictor）
 
-    It does not care about evaluations of the model, which is different from Tester.
-    This is a high-level model wrapper to be called by FastNLP.
-    This class does not share any operations with Trainer and Tester.
-    Currently, Predictor does not support GPU.
+    与测试器（Tester）不同的是，predictor不关心模型性能的评价指标，只做inference。
+    这是一个fastNLP调用的高级模型包装器。它与Trainer、Tester不共享任何操作。
+
+    :param torch.nn.Module network: 用来完成预测任务的模型
     """
 
     def __init__(self, network):
@@ -30,18 +30,19 @@ class Predictor(object):
         self.batch_size = 1
         self.batch_output = []
 
-    def predict(self, data, seq_len_field_name=None):
-        """Perform inference using the trained model.
+    def predict(self, data: DataSet, seq_len_field_name=None):
+        """用已经训练好的模型进行inference.
 
-        :param data: a DataSet object.
-        :param str seq_len_field_name: field name indicating sequence lengths
-        :return: list of batch outputs
+        :param fastNLP.DataSet data: 待预测的数据集
+        :param str seq_len_field_name: 表示序列长度信息的field名字
+        :return: dict dict里面的内容为模型预测的结果
         """
         if not isinstance(data, DataSet):
             raise ValueError("Only Dataset class is allowed, not {}.".format(type(data)))
         if seq_len_field_name is not None and seq_len_field_name not in data.field_arrays:
             raise ValueError("Field name {} not found in DataSet {}.".format(seq_len_field_name, data))
 
+        prev_training = self.network.training
         self.network.eval()
         network_device = _get_model_device(self.network)
         batch_output = defaultdict(list)
@@ -74,4 +75,5 @@ class Predictor(object):
                         else:
                             batch_output[key].append(value)
 
+        self.network.train(prev_training)
         return batch_output