diff --git a/fastNLP/core/predictor.py b/fastNLP/core/predictor.py
new file mode 100644
index 00000000..2d6a7380
--- /dev/null
+++ b/fastNLP/core/predictor.py
@@ -0,0 +1,79 @@
+"""
+    ..todo::
+        检查这个类是否需要
+"""
+from collections import defaultdict
+
+import torch
+
+from . import DataSetIter
+from . import DataSet
+from . import SequentialSampler
+from .utils import _build_args, _move_dict_value_to_device, _get_model_device
+
+
+class Predictor(object):
+    """
+    一个根据训练模型预测输出的预测器（Predictor）
+
+    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):
+        if not isinstance(network, torch.nn.Module):
+            raise ValueError(
+                "Only fastNLP.models.BaseModel or torch.nn,Module is allowed, not {}".format(type(network)))
+        self.network = network
+        self.batch_size = 1
+        self.batch_output = []
+
+    def predict(self, data: DataSet, seq_len_field_name=None):
+        """用已经训练好的模型进行inference.
+
+        :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)
+        data_iterator = DataSetIter(data, batch_size=self.batch_size, sampler=SequentialSampler(), as_numpy=False)
+
+        if hasattr(self.network, "predict"):
+            predict_func = self.network.predict
+        else:
+            predict_func = self.network.forward
+
+        with torch.no_grad():
+            for batch_x, _ in data_iterator:
+                _move_dict_value_to_device(batch_x, _, device=network_device)
+                refined_batch_x = _build_args(predict_func, **batch_x)
+                prediction = predict_func(**refined_batch_x)
+
+                if seq_len_field_name is not None:
+                    seq_lens = batch_x[seq_len_field_name].tolist()
+
+                for key, value in prediction.items():
+                    value = value.cpu().numpy()
+                    if len(value.shape) == 1 or (len(value.shape) == 2 and value.shape[1] == 1):
+                        batch_output[key].extend(value.tolist())
+                    else:
+                        if seq_len_field_name is not None:
+                            tmp_batch = []
+                            for idx, seq_len in enumerate(seq_lens):
+                                tmp_batch.append(value[idx, :seq_len])
+                            batch_output[key].extend(tmp_batch)
+                        else:
+                            batch_output[key].append(value)
+
+        self.network.train(prev_training)
+        return batch_output
diff --git a/test/core/test_predictor.py b/test/core/test_predictor.py
new file mode 100644
index 00000000..701353dc
--- /dev/null
+++ b/test/core/test_predictor.py
@@ -0,0 +1,48 @@
+import unittest
+from collections import defaultdict
+
+import numpy as np
+import torch
+
+from fastNLP.core.dataset import DataSet
+from fastNLP.core.instance import Instance
+from fastNLP.core.predictor import Predictor
+
+
+def prepare_fake_dataset():
+    mean = np.array([-3, -3])
+    cov = np.array([[1, 0], [0, 1]])
+    class_A = np.random.multivariate_normal(mean, cov, size=(1000,))
+
+    mean = np.array([3, 3])
+    cov = np.array([[1, 0], [0, 1]])
+    class_B = np.random.multivariate_normal(mean, cov, size=(1000,))
+
+    data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] +
+                       [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
+    return data_set
+
+
+class LinearModel(torch.nn.Module):
+    def __init__(self):
+        super(LinearModel, self).__init__()
+        self.linear = torch.nn.Linear(2, 1)
+
+    def forward(self, x):
+        return {"predict": self.linear(x)}
+
+
+class TestPredictor(unittest.TestCase):
+    def test_simple(self):
+        model = LinearModel()
+        predictor = Predictor(model)
+        data = prepare_fake_dataset()
+        data.set_input("x")
+        ans = predictor.predict(data)
+        self.assertTrue(isinstance(ans, defaultdict))
+        self.assertTrue("predict" in ans)
+        self.assertTrue(isinstance(ans["predict"], list))
+
+    def test_sequence(self):
+        # test sequence input/output
+        pass