diff --git a/README.md b/README.md
index 0c0dae459112d436e443dc14d1a9207a0b2638a5..fe2241c5312949febfce7005b1e885ad1665f0fc 100644
--- a/README.md
+++ b/README.md
@@ -177,6 +177,7 @@ python -m paddlerec.run -m ./models/rank/dnn/config.yaml -b backend.yaml
 |  多任务  |                  [ESMM](models/multitask/esmm/model.py)                   |      ✓      |      ✓      |       ✓       |
 |  多任务  |                  [MMOE](models/multitask/mmoe/model.py)                   |      ✓      |      ✓      |       ✓       |
 |  多任务  |           [ShareBottom](models/multitask/share-bottom/model.py)           |      ✓      |      ✓      |       ✓       |
+|  重排序  |           [Listwise](models/rerank/listwise/model.py)           |      ✓      |      x      |       ✓       |
 
 
 
diff --git a/core/model.py b/core/model.py
index 82b41ebc4b7ea752e708b9d7246b6bf7d5025db4..847a5d23362e4c3db8ef0cfa59fb83c4ed9a4c91 100755
--- a/core/model.py
+++ b/core/model.py
@@ -37,6 +37,10 @@ class Model(object):
         self._fetch_interval = 20
         self._namespace = "train.model"
         self._platform = envs.get_platform()
+        self._init_hyper_parameters()
+
+    def _init_hyper_parameters(self):
+        pass
 
     def _init_slots(self):
         sparse_slots = envs.get_global_env("sparse_slots", None,
@@ -129,12 +133,65 @@ class Model(object):
         print(">>>>>>>>>>>.learnig rate: %s" % learning_rate)
         return self._build_optimizer(optimizer, learning_rate)
 
-    @abc.abstractmethod
+    def input_data(self, is_infer=False):
+        sparse_slots = envs.get_global_env("sparse_slots", None,
+                                           "train.reader")
+        dense_slots = envs.get_global_env("dense_slots", None, "train.reader")
+        if sparse_slots is not None or dense_slots is not None:
+            sparse_slots = sparse_slots.strip().split(" ")
+            dense_slots = dense_slots.strip().split(" ")
+            dense_slots_shape = [[
+                int(j) for j in i.split(":")[1].strip("[]").split(",")
+            ] for i in dense_slots]
+            dense_slots = [i.split(":")[0] for i in dense_slots]
+            self._dense_data_var = []
+            data_var_ = []
+            for i in range(len(dense_slots)):
+                l = fluid.layers.data(
+                    name=dense_slots[i],
+                    shape=dense_slots_shape[i],
+                    dtype="float32")
+                data_var_.append(l)
+                self._dense_data_var.append(l)
+            self._sparse_data_var = []
+            for name in sparse_slots:
+                l = fluid.layers.data(
+                    name=name, shape=[1], lod_level=1, dtype="int64")
+                data_var_.append(l)
+                self._sparse_data_var.append(l)
+            return data_var_
+
+        else:
+            return None
+
+    def net(self, is_infer=False):
+        return None
+
+    def _construct_reader(self, is_infer=False):
+        if is_infer:
+            self._infer_data_loader = fluid.io.DataLoader.from_generator(
+                feed_list=self._infer_data_var,
+                capacity=64,
+                use_double_buffer=False,
+                iterable=False)
+        else:
+            dataset_class = envs.get_global_env("dataset_class", None,
+                                                "train.reader")
+            if dataset_class == "DataLoader":
+                self._data_loader = fluid.io.DataLoader.from_generator(
+                    feed_list=self._data_var,
+                    capacity=64,
+                    use_double_buffer=False,
+                    iterable=False)
+
     def train_net(self):
-        """R
-        """
-        pass
+        input_data = self.input_data(is_infer=False)
+        self._data_var = input_data
+        self._construct_reader(is_infer=False)
+        self.net(input_data, is_infer=False)
 
-    @abc.abstractmethod
     def infer_net(self):
-        pass
+        input_data = self.input_data(is_infer=True)
+        self._infer_data_var = input_data
+        self._construct_reader(is_infer=True)
+        self.net(input_data, is_infer=True)
diff --git a/doc/imgs/listwise.png b/doc/imgs/listwise.png
new file mode 100644
index 0000000000000000000000000000000000000000..88e79fe4052273f349e707d14e2e0647c63caa03
Binary files /dev/null and b/doc/imgs/listwise.png differ
diff --git a/models/rank/dcn/config.yaml b/models/rank/dcn/config.yaml
index 6cd89d6aab9a1abba14d9fe7e3b737b76fb2221d..58c88f0cfed18e2dbbb19c9a097dbe9b6d61c814 100755
--- a/models/rank/dcn/config.yaml
+++ b/models/rank/dcn/config.yaml
@@ -22,7 +22,7 @@ train:
 
   reader:
     batch_size: 2
-    train_data_path: "{workspace}/data/slot_train"
+    train_data_path: "{workspace}/data/sample_data/train"
     feat_dict_name: "{workspace}/data/vocab"
     sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
     dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
@@ -35,7 +35,7 @@ train:
       l2_reg_cross: 0.00005
       dnn_use_bn: False
       clip_by_norm: 100.0
-      cat_feat_num: "{workspace}/data/cat_feature_num.txt"
+      cat_feat_num: "{workspace}/data/sample_data/cat_feature_num.txt"
       is_sparse: False
       is_test: False
       num_field: 39
diff --git a/models/rank/dcn/data/sample_data/cat_feature_num.txt b/models/rank/dcn/data/sample_data/cat_feature_num.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0c75fc39b5890c323d1f94d6cc5c60fa151ef2e0
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/cat_feature_num.txt
@@ -0,0 +1,26 @@
+C1 139
+C2 422
+C3 1548
+C4 1965
+C5 54
+C6 10
+C7 3213
+C8 81
+C9 3
+C10 2402
+C11 2246
+C12 1583
+C13 1911
+C14 24
+C15 2011
+C16 1731
+C17 9
+C18 1197
+C19 584
+C20 3
+C21 1652
+C22 8
+C23 14
+C24 1770
+C25 40
+C26 1349
diff --git a/models/rank/dcn/data/sample_data/train/sample_train.txt b/models/rank/dcn/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4aa6d249feecf542a5ce947f510bded60aa6414f
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/train/sample_train.txt
@@ -0,0 +1,10 @@
+label:0 I1:0.69314718056 I2:1.60943791243 I3:1.79175946923 I4:0.0 I5:7.23201033166 I6:1.60943791243 I7:2.77258872224 I8:1.09861228867 I9:5.20400668708 I10:0.69314718056 I11:1.09861228867 I12:0 I13:1.09861228867 C1:95 C2:398 C3:0 C4:0 C5:53 C6:1 C7:73 C8:71 C9:3 C10:1974 C11:832 C12:0 C13:875 C14:8 C15:1764 C16:0 C17:5 C18:390 C19:226 C20:1 C21:0 C22:0 C23:8 C24:1759 C25:1 C26:862
+label:0 I1:1.09861228867 I2:1.38629436112 I3:3.80666248977 I4:0.69314718056 I5:4.63472898823 I6:2.19722457734 I7:1.09861228867 I8:1.09861228867 I9:1.60943791243 I10:0.69314718056 I11:0.69314718056 I12:0 I13:1.60943791243 C1:95 C2:200 C3:1184 C4:1929 C5:53 C6:4 C7:1477 C8:2 C9:3 C10:1283 C11:1567 C12:1048 C13:271 C14:6 C15:1551 C16:899 C17:1 C18:162 C19:226 C20:2 C21:575 C22:0 C23:8 C24:1615 C25:1 C26:659
+label:0 I1:1.09861228867 I2:1.38629436112 I3:0.69314718056 I4:2.7080502011 I5:6.64378973315 I6:4.49980967033 I7:1.60943791243 I8:1.09861228867 I9:5.50533153593 I10:0.69314718056 I11:1.38629436112 I12:1.38629436112 I13:3.82864139649 C1:123 C2:378 C3:991 C4:197 C5:53 C6:1 C7:689 C8:2 C9:3 C10:245 C11:623 C12:1482 C13:887 C14:21 C15:106 C16:720 C17:3 C18:768 C19:0 C20:0 C21:1010 C22:1 C23:8 C24:720 C25:0 C26:0
+label:0 I1:0 I2:6.79905586206 I3:0 I4:0 I5:8.38776764398 I6:0 I7:0.0 I8:0.0 I9:0.0 I10:0 I11:0.0 I12:0 I13:0 C1:95 C2:227 C3:0 C4:219 C5:53 C6:4 C7:3174 C8:2 C9:3 C10:569 C11:1963 C12:0 C13:1150 C14:21 C15:1656 C16:0 C17:6 C18:584 C19:0 C20:0 C21:0 C22:0 C23:8 C24:954 C25:0 C26:0
+label:0 I1:1.38629436112 I2:1.09861228867 I3:0 I4:0.0 I5:1.09861228867 I6:0.0 I7:1.38629436112 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:121 C2:147 C3:0 C4:1356 C5:53 C6:7 C7:2120 C8:2 C9:3 C10:703 C11:1678 C12:1210 C13:1455 C14:8 C15:538 C16:1276 C17:6 C18:346 C19:0 C20:0 C21:944 C22:0 C23:10 C24:355 C25:0 C26:0
+label:0 I1:0 I2:1.09861228867 I3:0 I4:0 I5:9.45915167004 I6:0 I7:0.0 I8:0.0 I9:1.94591014906 I10:0 I11:0.0 I12:0 I13:0 C1:14 C2:75 C3:993 C4:480 C5:50 C6:6 C7:1188 C8:2 C9:3 C10:245 C11:1037 C12:1365 C13:1421 C14:21 C15:786 C16:5 C17:2 C18:555 C19:0 C20:0 C21:1408 C22:6 C23:7 C24:753 C25:0 C26:0
+label:0 I1:0 I2:1.60943791243 I3:1.09861228867 I4:0 I5:8.06117135969 I6:0 I7:0.0 I8:0.69314718056 I9:1.09861228867 I10:0 I11:0.0 I12:0 I13:0 C1:139 C2:343 C3:553 C4:828 C5:50 C6:4 C7:0 C8:2 C9:3 C10:245 C11:2081 C12:260 C13:455 C14:21 C15:122 C16:1159 C17:2 C18:612 C19:0 C20:0 C21:1137 C22:0 C23:1 C24:1583 C25:0 C26:0
+label:1 I1:0.69314718056 I2:2.07944154168 I3:1.09861228867 I4:0.0 I5:0.0 I6:0.0 I7:0.69314718056 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:95 C2:227 C3:0 C4:1567 C5:21 C6:7 C7:2496 C8:71 C9:3 C10:1913 C11:2212 C12:0 C13:673 C14:21 C15:1656 C16:0 C17:5 C18:584 C19:0 C20:0 C21:0 C22:0 C23:10 C24:954 C25:0 C26:0
+label:0 I1:0 I2:3.87120101091 I3:1.60943791243 I4:2.19722457734 I5:9.85277303799 I6:5.52146091786 I7:3.36729582999 I8:3.4657359028 I9:4.9558270576 I10:0 I11:0.69314718056 I12:0 I13:2.19722457734 C1:14 C2:14 C3:454 C4:197 C5:53 C6:1 C7:1386 C8:2 C9:3 C10:0 C11:1979 C12:205 C13:214 C14:6 C15:1837 C16:638 C17:5 C18:6 C19:0 C20:0 C21:70 C22:0 C23:10 C24:720 C25:0 C26:0
+label:0 I1:0 I2:3.66356164613 I3:0 I4:0.69314718056 I5:10.4263800775 I6:3.09104245336 I7:0.69314718056 I8:1.09861228867 I9:1.38629436112 I10:0 I11:0.69314718056 I12:0 I13:0.69314718056 C1:14 C2:179 C3:120 C4:746 C5:53 C6:0 C7:1312 C8:2 C9:3 C10:1337 C11:1963 C12:905 C13:1150 C14:21 C15:1820 C16:328 C17:9 C18:77 C19:0 C20:0 C21:311 C22:0 C23:10 C24:89 C25:0 C26:0
diff --git a/models/rank/dcn/data/sample_data/vocab/C1.txt b/models/rank/dcn/data/sample_data/vocab/C1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4597891767100dcd85ac15caa833d119ba49bb05
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C1.txt
@@ -0,0 +1,139 @@
+f434fac1
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diff --git a/models/rank/dcn/data/sample_data/vocab/C10.txt b/models/rank/dcn/data/sample_data/vocab/C10.txt
new file mode 100644
index 0000000000000000000000000000000000000000..023078ebaaafbec5df4af3529a09e8b3fb8848f0
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C10.txt
@@ -0,0 +1,2402 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C11.txt b/models/rank/dcn/data/sample_data/vocab/C11.txt
new file mode 100644
index 0000000000000000000000000000000000000000..311221d155de8558a93f2f3f2f2ba652616f12fc
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C11.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C12.txt b/models/rank/dcn/data/sample_data/vocab/C12.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4553a544414464def71c643b5988b71614d6d338
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C12.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C13.txt b/models/rank/dcn/data/sample_data/vocab/C13.txt
new file mode 100644
index 0000000000000000000000000000000000000000..204761319cc5f7a33fed867a375c6c512247cf53
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C13.txt
@@ -0,0 +1,1911 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C14.txt b/models/rank/dcn/data/sample_data/vocab/C14.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a4a5a781ba6b052deb66e0dc44bdf98bfb1bbe24
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C14.txt
@@ -0,0 +1,24 @@
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diff --git a/models/rank/dcn/data/sample_data/vocab/C15.txt b/models/rank/dcn/data/sample_data/vocab/C15.txt
new file mode 100644
index 0000000000000000000000000000000000000000..93dfd19da6bf50714d87348bfd97b59857a281c4
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C15.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C16.txt b/models/rank/dcn/data/sample_data/vocab/C16.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8832f7160de90699622a3260b8c2d36467f833f7
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C16.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C17.txt b/models/rank/dcn/data/sample_data/vocab/C17.txt
new file mode 100644
index 0000000000000000000000000000000000000000..95c0fccec81cab5dfc06bf906492566a043fbfe1
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C17.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C18.txt b/models/rank/dcn/data/sample_data/vocab/C18.txt
new file mode 100644
index 0000000000000000000000000000000000000000..152a873922d041f038a54f78e11022c0624d4fb9
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C19.txt b/models/rank/dcn/data/sample_data/vocab/C19.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fd141ed95d49f963bfd871d6dc3ac4219b57da0a
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C19.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C2.txt b/models/rank/dcn/data/sample_data/vocab/C2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..26f550d3753641756f72f4afbb6bb503e951f32e
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C2.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C20.txt b/models/rank/dcn/data/sample_data/vocab/C20.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7e2df90cf93e2c54611e62aef11f134357bdc613
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C20.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C21.txt b/models/rank/dcn/data/sample_data/vocab/C21.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3e3077b9b197194cd43e6b487489ea7d39ae66db
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C22.txt b/models/rank/dcn/data/sample_data/vocab/C22.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3b6f6a760862e95652851d248f4d7084eda46451
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C23.txt b/models/rank/dcn/data/sample_data/vocab/C23.txt
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index 0000000000000000000000000000000000000000..4cf222bf01de901752cede45e57ffbfb764c607e
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C24.txt b/models/rank/dcn/data/sample_data/vocab/C24.txt
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index 0000000000000000000000000000000000000000..ac7de137388ba7964349336c687cdd1dad0eed64
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C25.txt b/models/rank/dcn/data/sample_data/vocab/C25.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f23c2ee0cdd3de63cba3bd06d77e3042001f113f
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C26.txt b/models/rank/dcn/data/sample_data/vocab/C26.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1cb7d644ce8688720752f3a74f7494df109d824e
--- /dev/null
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diff --git a/models/rank/dcn/data/sample_data/vocab/C3.txt b/models/rank/dcn/data/sample_data/vocab/C3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a794b343b63ddc4d727a454a1485b5c557fdd679
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C3.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C4.txt b/models/rank/dcn/data/sample_data/vocab/C4.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed778fd9ca3eb6c3c22410e0ef9594bf26359303
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C4.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C6.txt b/models/rank/dcn/data/sample_data/vocab/C6.txt
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diff --git a/models/rank/dcn/data/sample_data/vocab/C8.txt b/models/rank/dcn/data/sample_data/vocab/C8.txt
new file mode 100644
index 0000000000000000000000000000000000000000..61c11ee5c5aabab0f917ffd6e76d39c82ddc90e5
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C8.txt
@@ -0,0 +1,81 @@
+f504a6f4
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diff --git a/models/rank/dcn/data/sample_data/vocab/C9.txt b/models/rank/dcn/data/sample_data/vocab/C9.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eddb3ff906a2aa4c5177be414452c9defbb1496b
--- /dev/null
+++ b/models/rank/dcn/data/sample_data/vocab/C9.txt
@@ -0,0 +1,3 @@
+7cc72ec2
+a18233ea
+a73ee510
diff --git a/models/rank/dcn/model.py b/models/rank/dcn/model.py
index 67447fedefd180649bb018a3ea23aea216c9a2b4..89113a315284845f094857a879d70156956d3065 100755
--- a/models/rank/dcn/model.py
+++ b/models/rank/dcn/model.py
@@ -122,7 +122,7 @@ class Model(ModelBase):
         return fluid.layers.reduce_sum(fluid.layers.square(w))
 
     def train_net(self):
-        self.model._init_slots()
+        self._init_slots()
         self.init_network()
 
         self.net_input = self._create_embedding_input()
@@ -163,6 +163,5 @@ class Model(ModelBase):
         optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
         return optimizer
 
-    def infer_net(self, parameter_list):
-        self.model._init_slots()
-        self.deepfm_net()
+    def infer_net(self):
+        self.train_net()
diff --git a/models/rank/deepfm/config.yaml b/models/rank/deepfm/config.yaml
index 21c6039ca65d092c899de560f6f47ff350cb14e7..956b65b0c13f9242e8c84156dcfc535cf7fffae7 100755
--- a/models/rank/deepfm/config.yaml
+++ b/models/rank/deepfm/config.yaml
@@ -22,8 +22,8 @@ train:
 
   reader:
     batch_size: 2
-    train_data_path: "{workspace}/data/slot_train_data"
-    feat_dict_name: "{workspace}/data/feat_dict_10.pkl2"
+    train_data_path: "{workspace}/data/sample_data/train"
+    feat_dict_name: "{workspace}/data/sample_data/feat_dict_10.pkl2"
     sparse_slots: "label feat_idx"
     dense_slots: "feat_value:39"
 
diff --git a/models/rank/deepfm/data/sample_data/feat_dict_10.pkl2 b/models/rank/deepfm/data/sample_data/feat_dict_10.pkl2
new file mode 100644
index 0000000000000000000000000000000000000000..962d552ab2a094b01c1223b8359dc594aa5bbef5
Binary files /dev/null and b/models/rank/deepfm/data/sample_data/feat_dict_10.pkl2 differ
diff --git a/models/rank/deepfm/data/sample_data/train/sample_train.txt b/models/rank/deepfm/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b0308e17f74efa4272e1871e86d03c236b1945a
--- /dev/null
+++ b/models/rank/deepfm/data/sample_data/train/sample_train.txt
@@ -0,0 +1,100 @@
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diff --git a/models/rank/deepfm/model.py b/models/rank/deepfm/model.py
index 0c87b8c869db27b055038e00fe9f6a2efbeb1e29..deb63c40f2aecc9ee6469f34338d858b09daf067 100755
--- a/models/rank/deepfm/model.py
+++ b/models/rank/deepfm/model.py
@@ -135,7 +135,7 @@ class Model(ModelBase):
                                             y_dnn)
 
     def train_net(self):
-        self.model._init_slots()
+        self._init_slots()
         self.deepfm_net()
 
         # ------------------------- Cost(logloss) --------------------------
@@ -162,6 +162,5 @@ class Model(ModelBase):
         optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
         return optimizer
 
-    def infer_net(self, parameter_list):
-        self.model._init_slots()
-        self.deepfm_net()
+    def infer_net(self):
+        self.train_net()
diff --git a/models/rank/dnn/config.yaml b/models/rank/dnn/config.yaml
index 16e0be0b8fb5535d495e9278879081b77b127fa0..ff59d9ef023da6dbaee01fda519abd9cfa75b1e3 100755
--- a/models/rank/dnn/config.yaml
+++ b/models/rank/dnn/config.yaml
@@ -23,7 +23,7 @@ train:
 
   reader:
     batch_size: 2
-    train_data_path: "{workspace}/data/slot_train_data"
+    train_data_path: "{workspace}/data/sample_data/train"
     reader_debug_mode: False
     sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
     dense_slots: "dense_var:13"
diff --git a/models/rank/dnn/data/sample_data/train/sample_train.txt b/models/rank/dnn/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b2b2e2022a8601d60b6e47ea9665dcc314bd04b6
--- /dev/null
+++ b/models/rank/dnn/data/sample_data/train/sample_train.txt
@@ -0,0 +1,80 @@
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.05 dense_feature:0.08 dense_feature:0.207421875 dense_feature:0.028 dense_feature:0.35 dense_feature:0.08 dense_feature:0.082 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.08 1:737395 2:210498 3:903564 4:286224 5:286835 6:906818 7:906116 8:67180 9:27346 10:51086 11:142177 12:95024 13:157883 14:873363 15:600281 16:812592 17:228085 18:35900 19:880474 20:984402 21:100885 22:26235 23:410878 24:798162 25:499868 26:306163
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+click:1 dense_feature:0.2 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.00440625 dense_feature:0.036 dense_feature:0.04 dense_feature:0.3 dense_feature:0.03 dense_feature:0.1 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:715353 2:532829 3:967094 4:707735 5:286835 6:948614 7:555710 8:154004 9:27346 10:708281 11:514992 12:158604 13:780149 14:255651 15:285282 16:149708 17:342789 18:553067 19:26230 20:26229 21:229985 22:26235 23:700326 24:777746 25:26224 26:26223
+click:1 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.23178125 dense_feature:0.222 dense_feature:0.06 dense_feature:0.0 dense_feature:0.408 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.0 1:715353 2:227084 3:456811 4:828682 5:286835 6:948614 7:406567 8:67180 9:27346 10:66123 11:598531 12:527138 13:731439 14:813514 15:35257 16:43339 17:342789 18:918487 19:26230 20:26229 21:580653 22:26235 23:410878 24:495283 25:26224 26:26223
+click:0 dense_feature:0.15 dense_feature:0.462686567164 dense_feature:0.08 dense_feature:0.22 dense_feature:0.00015625 dense_feature:0.022 dense_feature:0.03 dense_feature:0.52 dense_feature:0.022 dense_feature:0.1 dense_feature:0.1 dense_feature:0.0 dense_feature:0.22 1:576931 2:99294 3:263211 4:501662 5:938478 6:154084 7:128918 8:67180 9:27346 10:912799 11:801006 12:506258 13:378182 14:201966 15:150934 16:240427 17:681378 18:393279 19:26230 20:26229 21:152038 22:26235 23:700326 24:551443 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.181484375 dense_feature:0.06 dense_feature:0.01 dense_feature:0.0 dense_feature:0.056 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:230803 2:283434 3:367596 4:197992 5:938478 6:948614 7:268098 8:67180 9:27346 10:870993 11:632267 12:139817 13:718764 14:255651 15:884839 16:80117 17:276641 18:556463 19:880474 20:537425 21:271358 22:26235 23:410878 24:488077 25:253255 26:584828
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.0 dense_feature:0.16 dense_feature:4.790078125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.28 dense_feature:0.016 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.2 1:737395 2:532829 3:158777 4:112926 5:286835 6:948614 7:277312 8:67180 9:502607 10:708281 11:755513 12:524443 13:4029 14:873363 15:503814 16:146094 17:644532 18:121590 19:26230 20:26229 21:901094 22:191474 23:744389 24:259263 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:3.30845771144 dense_feature:0.0 dense_feature:0.04 dense_feature:0.022671875 dense_feature:0.062 dense_feature:0.01 dense_feature:0.4 dense_feature:0.062 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:663372 2:529436 3:511823 4:942782 5:286835 6:906818 7:190054 8:67180 9:27346 10:708281 11:32527 12:494263 13:652478 14:873363 15:616057 16:17325 17:342789 18:325238 19:26230 20:26229 21:256747 22:179327 23:410878 24:169709 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.01 dense_feature:0.16 dense_feature:0.206765625 dense_feature:0.328 dense_feature:0.13 dense_feature:0.16 dense_feature:0.176 dense_feature:0.0 dense_feature:0.7 dense_feature:0.0 dense_feature:0.16 1:737395 2:552854 3:606082 4:267619 5:286835 6:948614 7:918889 8:67180 9:27346 10:708281 11:400024 12:972010 13:66330 14:255651 15:432931 16:650209 17:506108 18:212910 19:26230 20:26229 21:107726 22:26235 23:410878 24:718419 25:26224 26:26223
diff --git a/models/rank/dnn/model.py b/models/rank/dnn/model.py
index d7ab801f38fdffbdeb0ca5259abaec37136d3fc9..644bbd7c98ec8360faa22969e245f284946947d8 100755
--- a/models/rank/dnn/model.py
+++ b/models/rank/dnn/model.py
@@ -24,40 +24,33 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def input(self):
+    def _init_hyper_parameters(self):
+        self.is_distributed = True if envs.get_trainer(
+        ) == "CtrTrainer" else False
+        self.sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number", None, self._namespace)
+        self.sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim", None, self._namespace)
+        self.learning_rate = envs.get_global_env(
+            "hyper_parameters.learning_rate", None, self._namespace)
+
+    def net(self, input, is_infer=False):
         self.sparse_inputs = self._sparse_data_var[1:]
         self.dense_input = self._dense_data_var[0]
         self.label_input = self._sparse_data_var[0]
 
-    def net(self):
-        is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
-        sparse_feature_number = envs.get_global_env(
-            "hyper_parameters.sparse_feature_number", None, self._namespace)
-        sparse_feature_dim = envs.get_global_env(
-            "hyper_parameters.sparse_feature_dim", None, self._namespace)
-
         def embedding_layer(input):
             emb = fluid.layers.embedding(
                 input=input,
                 is_sparse=True,
-                is_distributed=is_distributed,
-                size=[sparse_feature_number, sparse_feature_dim],
+                is_distributed=self.is_distributed,
+                size=[self.sparse_feature_number, self.sparse_feature_dim],
                 param_attr=fluid.ParamAttr(
                     name="SparseFeatFactors",
                     initializer=fluid.initializer.Uniform()), )
             emb_sum = fluid.layers.sequence_pool(input=emb, pool_type='sum')
             return emb_sum
 
-        def fc(input, output_size):
-            output = fluid.layers.fc(
-                input=input,
-                size=output_size,
-                act='relu',
-                param_attr=fluid.ParamAttr(
-                    initializer=fluid.initializer.Normal(
-                        scale=1.0 / math.sqrt(input.shape[1]))))
-            return output
-
         sparse_embed_seq = list(map(embedding_layer, self.sparse_inputs))
         concated = fluid.layers.concat(
             sparse_embed_seq + [self.dense_input], axis=1)
@@ -67,7 +60,14 @@ class Model(ModelBase):
                                             self._namespace)
 
         for size in hidden_layers:
-            fcs.append(fc(fcs[-1], size))
+            output = fluid.layers.fc(
+                input=fcs[-1],
+                size=size,
+                act='relu',
+                param_attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.Normal(
+                        scale=1.0 / math.sqrt(fcs[-1].shape[1]))))
+            fcs.append(output)
 
         predict = fluid.layers.fc(
             input=fcs[-1],
@@ -78,13 +78,10 @@ class Model(ModelBase):
 
         self.predict = predict
 
-    def avg_loss(self):
         cost = fluid.layers.cross_entropy(
             input=self.predict, label=self.label_input)
         avg_cost = fluid.layers.reduce_mean(cost)
         self._cost = avg_cost
-
-    def metrics(self):
         auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
                                              label=self.label_input,
                                              num_thresholds=2**12,
@@ -92,20 +89,9 @@ class Model(ModelBase):
         self._metrics["AUC"] = auc
         self._metrics["BATCH_AUC"] = batch_auc
 
-    def train_net(self):
-        self.model._init_slots()
-        self.input()
-        self.net()
-        self.avg_loss()
-        self.metrics()
-
     def optimizer(self):
-        learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
-                                            None, self._namespace)
-        optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
+        optimizer = fluid.optimizer.Adam(self.learning_rate, lazy_mode=True)
         return optimizer
 
     def infer_net(self):
-        self.model._init_slots()
-        self.input()
-        self.net()
+        pass
diff --git a/models/rank/wide_deep/config.yaml b/models/rank/wide_deep/config.yaml
index 3babdddb3ab777a6f48a33894bf3ef2a79311cf9..9cadddf2b16989ef9d6844f6ac40dc53b06e4309 100755
--- a/models/rank/wide_deep/config.yaml
+++ b/models/rank/wide_deep/config.yaml
@@ -22,7 +22,7 @@ train:
 
   reader:
     batch_size: 2
-    train_data_path: "{workspace}/data/slot_train_data"
+    train_data_path: "{workspace}/data/sample_data/train"
     sparse_slots: "label"
     dense_slots: "wide_input:8 deep_input:58"
 
diff --git a/models/rank/wide_deep/data/sample_data/train/train_data.txt b/models/rank/wide_deep/data/sample_data/train/train_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..967b975d703d5aaa0a6e73e6fca74384c6e289dc
--- /dev/null
+++ b/models/rank/wide_deep/data/sample_data/train/train_data.txt
@@ -0,0 +1,500 @@
+wide_input:9.0 wide_input:4.0 wide_input:1.0 wide_input:7.0 wide_input:1.0 wide_input:3.0 wide_input:203.0 wide_input:643.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:39.0 deep_input:13.0 deep_input:2174.0 deep_input:0.0 deep_input:40.0 label:0
+wide_input:9.0 wide_input:2.0 wide_input:0.0 wide_input:6.0 wide_input:4.0 wide_input:5.0 wide_input:211.0 wide_input:980.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:50.0 deep_input:13.0 deep_input:0.0 deep_input:0.0 deep_input:13.0 label:0
+wide_input:11.0 wide_input:0.0 wide_input:1.0 wide_input:4.0 wide_input:6.0 wide_input:3.0 wide_input:36.0 wide_input:519.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:38.0 deep_input:9.0 deep_input:0.0 deep_input:0.0 deep_input:40.0 label:0
+wide_input:1.0 wide_input:2.0 wide_input:0.0 wide_input:4.0 wide_input:6.0 wide_input:6.0 wide_input:101.0 wide_input:1054.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:53.0 deep_input:7.0 deep_input:0.0 deep_input:0.0 deep_input:40.0 label:0
+wide_input:9.0 wide_input:2.0 wide_input:5.0 wide_input:4.0 wide_input:10.0 wide_input:1.0 wide_input:204.0 wide_input:320.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:1.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:0.0 deep_input:28.0 deep_input:13.0 deep_input:0.0 deep_input:0.0 deep_input:40.0 label:0
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diff --git a/models/rank/wide_deep/model.py b/models/rank/wide_deep/model.py
index a7d51d958c55f0368cdd7f9ff7baa51dd25a6f76..d798a54590d709c9f25f63638250bd12b0e62cbd 100755
--- a/models/rank/wide_deep/model.py
+++ b/models/rank/wide_deep/model.py
@@ -57,7 +57,7 @@ class Model(ModelBase):
         return l3
 
     def train_net(self):
-        self.model._init_slots()
+        self._init_slots()
         wide_input = self._dense_data_var[0]
         deep_input = self._dense_data_var[1]
         label = self._sparse_data_var[0]
@@ -122,6 +122,5 @@ class Model(ModelBase):
         optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
         return optimizer
 
-    def infer_net(self, parameter_list):
-        self.model._init_slots()
-        self.deepfm_net()
+    def infer_net(self):
+        self.train_net()
diff --git a/models/rank/xdeepfm/config.yaml b/models/rank/xdeepfm/config.yaml
index 5f60a141a38ab388688a24da2ea153c2b576ccaa..37b6b65b4777b7a2d497cfd1c0213c3e88fe6baa 100755
--- a/models/rank/xdeepfm/config.yaml
+++ b/models/rank/xdeepfm/config.yaml
@@ -22,7 +22,7 @@ train:
 
   reader:
     batch_size: 2
-    train_data_path: "{workspace}/data/slot_train_data"
+    train_data_path: "{workspace}/data/sample_data/train"
     sparse_slots: "label feat_idx"
     dense_slots: "feat_value:39"
 
diff --git a/models/rank/xdeepfm/data/sample_data/train/sample_train.txt b/models/rank/xdeepfm/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b0308e17f74efa4272e1871e86d03c236b1945a
--- /dev/null
+++ b/models/rank/xdeepfm/data/sample_data/train/sample_train.txt
@@ -0,0 +1,100 @@
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+feat_idx:0 feat_idx:2 feat_idx:0 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:0 feat_idx:11 feat_idx:12 feat_idx:13 feat_idx:506931 feat_idx:123566 feat_idx:961529 feat_idx:810019 feat_idx:314332 feat_idx:615411 feat_idx:475867 feat_idx:795081 feat_idx:148475 feat_idx:697060 feat_idx:1069621 feat_idx:370551 feat_idx:696973 feat_idx:69630 feat_idx:396064 feat_idx:95177 feat_idx:854924 feat_idx:488825 feat_idx:0 feat_idx:0 feat_idx:581782 feat_idx:0 feat_idx:476211 feat_idx:289148 feat_idx:0 feat_idx:0 feat_value:0.0 feat_value:0.0066672358525 feat_value:0.0 feat_value:0.00103199174407 feat_value:0.000325784854359 feat_value:4.40797425743e-05 feat_value:0.000266377794747 feat_value:0.000165371258475 feat_value:0.00299803576967 feat_value:0.0 feat_value:0.030303030303 feat_value:0.0 feat_value:0.000135263086704 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 label:0
+feat_idx:1 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:10 feat_idx:11 feat_idx:0 feat_idx:13 feat_idx:268086 feat_idx:704711 feat_idx:995515 feat_idx:139394 feat_idx:943087 feat_idx:0 feat_idx:546815 feat_idx:144963 feat_idx:148475 feat_idx:364765 feat_idx:552750 feat_idx:920037 feat_idx:816538 feat_idx:223357 feat_idx:790588 feat_idx:560935 feat_idx:13161 feat_idx:750233 feat_idx:734534 feat_idx:1047606 feat_idx:361734 feat_idx:0 feat_idx:122096 feat_idx:434883 feat_idx:502861 feat_idx:203213 feat_value:0.0 feat_value:0.000197921436832 feat_value:4.57770656901e-05 feat_value:0.00206398348813 feat_value:0.000625316933178 feat_value:0.000874634892132 feat_value:0.000142068157198 feat_value:0.000330742516951 feat_value:0.00975223129674 feat_value:0.0 feat_value:0.00865800865801 feat_value:0.0 feat_value:0.000270526173407 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 label:0
diff --git a/models/rank/xdeepfm/model.py b/models/rank/xdeepfm/model.py
index d1045897d9cb4ca5b7018a1dcb9da726829c4744..23443c7d79e78690e6669716901238710599e3b7 100755
--- a/models/rank/xdeepfm/model.py
+++ b/models/rank/xdeepfm/model.py
@@ -154,7 +154,7 @@ class Model(ModelBase):
         self.predict = fluid.layers.sigmoid(y_linear + y_cin + y_dnn)
 
     def train_net(self):
-        self.model._init_slots()
+        self._init_slots()
         self.xdeepfm_net()
 
         cost = fluid.layers.log_loss(
@@ -179,6 +179,5 @@ class Model(ModelBase):
         optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
         return optimizer
 
-    def infer_net(self, parameter_list):
-        self.model._init_slots()
-        self.xdeepfm_net()
+    def infer_net(self):
+        self.train_net()
diff --git a/models/rerank/__init__.py b/models/rerank/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/rerank/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/rerank/listwise/__init__.py b/models/rerank/listwise/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/rerank/listwise/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/rerank/listwise/config.yaml b/models/rerank/listwise/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..18b018026634e461257d167fa543f2d81a25436c
--- /dev/null
+++ b/models/rerank/listwise/config.yaml
@@ -0,0 +1,55 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+evaluate:
+  reader:
+    batch_size: 1
+    class: "{workspace}/random_infer_reader.py"
+    test_data_path: "{workspace}/data/train"
+
+train:
+  trainer:
+    # for cluster training
+    strategy: "async"
+
+  epochs: 3
+  workspace: "paddlerec.models.rerank.listwise"
+  device: cpu
+
+  reader:
+    batch_size: 2
+    class: "{workspace}/random_reader.py"
+    train_data_path: "{workspace}/data/train"
+    dataset_class: "DataLoader"
+
+  model:
+    models: "{workspace}/model.py"
+    hyper_parameters:
+      hidden_size: 128
+      user_vocab: 200
+      item_vocab: 1000
+      item_len: 5
+      embed_size: 16
+      learning_rate: 0.01
+      optimizer: sgd
+
+  save:
+    increment:
+      dirname: "increment"
+      epoch_interval: 2
+      save_last: True
+    inference:
+      dirname: "inference"
+      epoch_interval: 4
+      save_last: True
diff --git a/models/rerank/listwise/data/test/small_data.txt b/models/rerank/listwise/data/test/small_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3c4cf5f84f66594e76603cce1f18d211ebd05a7
--- /dev/null
+++ b/models/rerank/listwise/data/test/small_data.txt
@@ -0,0 +1,100 @@
+4764,174,1
+4764,2958,0
+4764,452,0
+4764,1946,0
+4764,3208,0
+2044,2237,1
+2044,1998,0
+2044,328,0
+2044,1542,0
+2044,1932,0
+4276,65,1
+4276,3247,0
+4276,942,0
+4276,3666,0
+4276,2222,0
+3933,682,1
+3933,2451,0
+3933,3695,0
+3933,1643,0
+3933,3568,0
+1151,1265,1
+1151,118,0
+1151,2532,0
+1151,2083,0
+1151,2350,0
+1757,876,1
+1757,201,0
+1757,3633,0
+1757,1068,0
+1757,2549,0
+3370,276,1
+3370,2435,0
+3370,606,0
+3370,910,0
+3370,2146,0
+5137,1018,1
+5137,2163,0
+5137,3167,0
+5137,2315,0
+5137,3595,0
+3933,2831,1
+3933,2881,0
+3933,2949,0
+3933,3660,0
+3933,417,0
+3102,999,1
+3102,1902,0
+3102,2161,0
+3102,3042,0
+3102,1113,0
+2022,336,1
+2022,1672,0
+2022,2656,0
+2022,3649,0
+2022,883,0
+2664,655,1
+2664,3660,0
+2664,1711,0
+2664,3386,0
+2664,1668,0
+25,701,1
+25,32,0
+25,2482,0
+25,3177,0
+25,2767,0
+1738,1643,1
+1738,2187,0
+1738,228,0
+1738,650,0
+1738,3101,0
+5411,1241,1
+5411,2546,0
+5411,3019,0
+5411,3618,0
+5411,1674,0
+638,579,1
+638,3512,0
+638,783,0
+638,2111,0
+638,1880,0
+3554,200,1
+3554,2893,0
+3554,2428,0
+3554,969,0
+3554,2741,0
+4283,1074,1
+4283,3056,0
+4283,2032,0
+4283,405,0
+4283,1505,0
+5111,200,1
+5111,3488,0
+5111,477,0
+5111,2790,0
+5111,40,0
+3964,515,1
+3964,1528,0
+3964,2173,0
+3964,1701,0
+3964,2832,0
diff --git a/models/rerank/listwise/data/train/small_data.txt b/models/rerank/listwise/data/train/small_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3c4cf5f84f66594e76603cce1f18d211ebd05a7
--- /dev/null
+++ b/models/rerank/listwise/data/train/small_data.txt
@@ -0,0 +1,100 @@
+4764,174,1
+4764,2958,0
+4764,452,0
+4764,1946,0
+4764,3208,0
+2044,2237,1
+2044,1998,0
+2044,328,0
+2044,1542,0
+2044,1932,0
+4276,65,1
+4276,3247,0
+4276,942,0
+4276,3666,0
+4276,2222,0
+3933,682,1
+3933,2451,0
+3933,3695,0
+3933,1643,0
+3933,3568,0
+1151,1265,1
+1151,118,0
+1151,2532,0
+1151,2083,0
+1151,2350,0
+1757,876,1
+1757,201,0
+1757,3633,0
+1757,1068,0
+1757,2549,0
+3370,276,1
+3370,2435,0
+3370,606,0
+3370,910,0
+3370,2146,0
+5137,1018,1
+5137,2163,0
+5137,3167,0
+5137,2315,0
+5137,3595,0
+3933,2831,1
+3933,2881,0
+3933,2949,0
+3933,3660,0
+3933,417,0
+3102,999,1
+3102,1902,0
+3102,2161,0
+3102,3042,0
+3102,1113,0
+2022,336,1
+2022,1672,0
+2022,2656,0
+2022,3649,0
+2022,883,0
+2664,655,1
+2664,3660,0
+2664,1711,0
+2664,3386,0
+2664,1668,0
+25,701,1
+25,32,0
+25,2482,0
+25,3177,0
+25,2767,0
+1738,1643,1
+1738,2187,0
+1738,228,0
+1738,650,0
+1738,3101,0
+5411,1241,1
+5411,2546,0
+5411,3019,0
+5411,3618,0
+5411,1674,0
+638,579,1
+638,3512,0
+638,783,0
+638,2111,0
+638,1880,0
+3554,200,1
+3554,2893,0
+3554,2428,0
+3554,969,0
+3554,2741,0
+4283,1074,1
+4283,3056,0
+4283,2032,0
+4283,405,0
+4283,1505,0
+5111,200,1
+5111,3488,0
+5111,477,0
+5111,2790,0
+5111,40,0
+3964,515,1
+3964,1528,0
+3964,2173,0
+3964,1701,0
+3964,2832,0
diff --git a/models/rerank/listwise/model.py b/models/rerank/listwise/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4cf9d8ed1a669d6d1ff3339008605f1aa26f4cd
--- /dev/null
+++ b/models/rerank/listwise/model.py
@@ -0,0 +1,223 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+    def __init__(self, config):
+        ModelBase.__init__(self, config)
+
+    def _init_hyper_parameters(self):
+        self.item_len = envs.get_global_env("hyper_parameters.self.item_len",
+                                            None, self._namespace)
+        self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size",
+                                               None, self._namespace)
+        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
+                                              None, self._namespace)
+        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
+                                              None, self._namespace)
+        self.embed_size = envs.get_global_env("hyper_parameters.embed_size",
+                                              None, self._namespace)
+
+    def input_data(self, is_infer=False):
+        user_slot_names = fluid.data(
+            name='user_slot_names',
+            shape=[None, 1],
+            dtype='int64',
+            lod_level=1)
+        item_slot_names = fluid.data(
+            name='item_slot_names',
+            shape=[None, self.item_len],
+            dtype='int64',
+            lod_level=1)
+        lens = fluid.data(name='lens', shape=[None], dtype='int64')
+        labels = fluid.data(
+            name='labels',
+            shape=[None, self.item_len],
+            dtype='int64',
+            lod_level=1)
+
+        inputs = [user_slot_names] + [item_slot_names] + [lens] + [labels]
+
+        # demo: hot to use is_infer:
+        if is_infer:
+            return inputs
+        else:
+            return inputs
+
+    def net(self, inputs, is_infer=False):
+        # user encode
+        user_embedding = fluid.embedding(
+            input=inputs[0],
+            size=[self.user_vocab, self.embed_size],
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Xavier(),
+                regularizer=fluid.regularizer.L2Decay(1e-5)),
+            is_sparse=True)
+
+        user_feature = fluid.layers.fc(
+            input=user_embedding,
+            size=self.hidden_size,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormal(
+                    loc=0.0, scale=np.sqrt(1.0 / self.hidden_size))),
+            bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
+                value=0.0)),
+            act='relu',
+            name='user_feature_fc')
+        # item encode
+        item_embedding = fluid.embedding(
+            input=inputs[1],
+            size=[self.item_vocab, self.embed_size],
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Xavier(),
+                regularizer=fluid.regularizer.L2Decay(1e-5)),
+            is_sparse=True)
+
+        item_embedding = fluid.layers.sequence_unpad(
+            x=item_embedding, length=inputs[2])
+
+        item_fc = fluid.layers.fc(
+            input=item_embedding,
+            size=self.hidden_size,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormal(
+                    loc=0.0, scale=np.sqrt(1.0 / self.hidden_size))),
+            bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
+                value=0.0)),
+            act='relu',
+            name='item_fc')
+
+        pos = self._fluid_sequence_get_pos(item_fc)
+        pos_embed = fluid.embedding(
+            input=pos,
+            size=[self.user_vocab, self.embed_size],
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Xavier(),
+                regularizer=fluid.regularizer.L2Decay(1e-5)),
+            is_sparse=True)
+
+        pos_embed = fluid.layers.squeeze(pos_embed, [1])
+
+        # item gru
+        gru_input = fluid.layers.fc(
+            input=fluid.layers.concat([item_fc, pos_embed], 1),
+            size=self.hidden_size * 3,
+            name='item_gru_fc')
+
+        # forward gru
+        item_gru_forward = fluid.layers.dynamic_gru(
+            input=gru_input,
+            size=self.hidden_size,
+            is_reverse=False,
+            h_0=user_feature)
+        # backward gru
+        item_gru_backward = fluid.layers.dynamic_gru(
+            input=gru_input,
+            size=self.hidden_size,
+            is_reverse=True,
+            h_0=user_feature)
+
+        item_gru = fluid.layers.concat(
+            [item_gru_forward, item_gru_backward], axis=1)
+
+        out_click_fc1 = fluid.layers.fc(
+            input=item_gru,
+            size=self.hidden_size,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormal(
+                    loc=0.0, scale=np.sqrt(1.0 / self.hidden_size))),
+            bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
+                value=0.0)),
+            act='relu',
+            name='out_click_fc1')
+
+        click_prob = fluid.layers.fc(input=out_click_fc1,
+                                     size=2,
+                                     act='softmax',
+                                     name='out_click_fc2')
+
+        labels = fluid.layers.sequence_unpad(x=inputs[3], length=inputs[2])
+
+        auc_val, batch_auc, auc_states = fluid.layers.auc(input=click_prob,
+                                                          label=labels)
+
+        if is_infer:
+            self._infer_results["AUC"] = auc_val
+            return
+
+        loss = fluid.layers.reduce_mean(
+            fluid.layers.cross_entropy(
+                input=click_prob, label=labels))
+        self._cost = loss
+        self._metrics['auc'] = auc_val
+
+    def _fluid_sequence_pad(self, input, pad_value, maxlen=None):
+        """
+        args:
+            input: (batch*seq_len, dim)
+        returns:
+            (batch, max_seq_len, dim)
+        """
+        pad_value = fluid.layers.cast(
+            fluid.layers.assign(input=np.array([pad_value], 'float32')),
+            input.dtype)
+        input_padded, _ = fluid.layers.sequence_pad(
+            input, pad_value,
+            maxlen=maxlen)  # (batch, max_seq_len, 1), (batch, 1)
+        # TODO, maxlen=300, used to solve issues: https://github.com/PaddlePaddle/Paddle/issues/14164
+        return input_padded
+
+    def _fluid_sequence_get_pos(self, lodtensor):
+        """
+        args:
+            lodtensor: lod = [[0,4,7]]
+        return:
+            pos: lod = [[0,4,7]]
+                 data = [0,1,2,3,0,1,3]
+                 shape = [-1, 1]
+        """
+        lodtensor = fluid.layers.reduce_sum(lodtensor, dim=1, keep_dim=True)
+        assert lodtensor.shape == (-1, 1), (lodtensor.shape())
+        ones = fluid.layers.cast(lodtensor * 0 + 1,
+                                 'float32')  # (batch*seq_len, 1)
+        ones_padded = self._fluid_sequence_pad(ones,
+                                               0)  # (batch, max_seq_len, 1)
+        ones_padded = fluid.layers.squeeze(ones_padded,
+                                           [2])  # (batch, max_seq_len)
+        seq_len = fluid.layers.cast(
+            fluid.layers.reduce_sum(
+                ones_padded, 1, keep_dim=True), 'int64')  # (batch, 1)
+        seq_len = fluid.layers.squeeze(seq_len, [1])
+
+        pos = fluid.layers.cast(
+            fluid.layers.cumsum(
+                ones_padded, 1, exclusive=True), 'int64')
+        pos = fluid.layers.sequence_unpad(pos, seq_len)  # (batch*seq_len, 1)
+        pos.stop_gradient = True
+        return pos
+
+    #def train_net(self):
+    #    input_data = self.input_data()
+    #    self.net(input_data)
+
+    #def infer_net(self):
+    #    input_data = self.input_data(is_infer=True)
+    #    self.net(input_data, is_infer=True)
diff --git a/models/rerank/listwise/random_infer_reader.py b/models/rerank/listwise/random_infer_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f93688e59dd2dc142a0ab79201278b25e18b468
--- /dev/null
+++ b/models/rerank/listwise/random_infer_reader.py
@@ -0,0 +1,68 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+from collections import defaultdict
+
+
+class EvaluateReader(Reader):
+    def init(self):
+        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
+                                              None, "train.model")
+        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
+                                              None, "train.model")
+        self.item_len = envs.get_global_env("hyper_parameters.item_len", None,
+                                            "train.model")
+        self.batch_size = envs.get_global_env("batch_size", None,
+                                              "train.reader")
+
+    def reader_creator(self):
+        def reader():
+            user_slot_name = []
+            for j in range(self.batch_size):
+                user_slot_name.append(
+                    [int(np.random.randint(self.user_vocab))])
+            item_slot_name = np.random.randint(
+                self.item_vocab, size=(self.batch_size,
+                                       self.item_len)).tolist()
+            length = [self.item_len] * self.batch_size
+            label = np.random.randint(
+                2, size=(self.batch_size, self.item_len)).tolist()
+            output = [user_slot_name, item_slot_name, length, label]
+
+            yield output
+
+        return reader
+
+    def generate_batch_from_trainfiles(self, files):
+        return fluid.io.batch(
+            self.reader_creator(), batch_size=self.batch_size)
+
+    def generate_sample(self, line):
+        """
+        the file is not used
+        """
+
+        def reader():
+            """
+            This function needs to be implemented by the user, based on data format
+            """
+            pass
+
+        return reader
diff --git a/models/rerank/listwise/random_reader.py b/models/rerank/listwise/random_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..41cf14b79285efe8f2d80e01bba74da3501cc504
--- /dev/null
+++ b/models/rerank/listwise/random_reader.py
@@ -0,0 +1,68 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+from collections import defaultdict
+
+
+class TrainReader(Reader):
+    def init(self):
+        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
+                                              None, "train.model")
+        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
+                                              None, "train.model")
+        self.item_len = envs.get_global_env("hyper_parameters.item_len", None,
+                                            "train.model")
+        self.batch_size = envs.get_global_env("batch_size", None,
+                                              "train.reader")
+
+    def reader_creator(self):
+        def reader():
+            user_slot_name = []
+            for j in range(self.batch_size):
+                user_slot_name.append(
+                    [int(np.random.randint(self.user_vocab))])
+            item_slot_name = np.random.randint(
+                self.item_vocab, size=(self.batch_size,
+                                       self.item_len)).tolist()
+            length = [self.item_len] * self.batch_size
+            label = np.random.randint(
+                2, size=(self.batch_size, self.item_len)).tolist()
+            output = [user_slot_name, item_slot_name, length, label]
+
+            yield output
+
+        return reader
+
+    def generate_batch_from_trainfiles(self, files):
+        return fluid.io.batch(
+            self.reader_creator(), batch_size=self.batch_size)
+
+    def generate_sample(self, line):
+        """
+        the file is not used
+        """
+
+        def reader():
+            """
+            This function needs to be implemented by the user, based on data format
+            """
+            pass
+
+        return reader
diff --git a/models/rerank/readme.md b/models/rerank/readme.md
new file mode 100755
index 0000000000000000000000000000000000000000..e7552c377dd03ab93af5c233ef8be31edc529de4
--- /dev/null
+++ b/models/rerank/readme.md
@@ -0,0 +1,43 @@
+# 重排序模型库
+
+## 简介
+我们提供了常见的重排序使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。目前实现的模型是 [Listwise](listwise)。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+    * [重排序模型列表](#重排序模型列表)
+* [使用教程](#使用教程)
+    * [训练 预测](#训练 预测)
+* [效果对比](#效果对比)
+    * [模型效果列表](#模型效果列表)
+
+## 整体介绍
+### 融合模型列表
+
+|       模型        |       简介        |       论文        |
+| :------------------: | :--------------------: | :---------: |
+| Listwise | Listwise | [Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf)(2019) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+
+[Listwise](https://arxiv.org/pdf/1902.00245.pdf):
+<p align="center">
+<img align="center" src="../../doc/imgs/listwise.png">
+<p>
+
+
+## 使用教程
+### 训练 预测
+```shell
+python -m paddlerec.run -m paddlerec.models.rerank.listwise # listwise
+```
+
+## 效果对比
+### 模型效果列表
+
+|       数据集        |       模型       |       loss        |       auc       | 
+| :------------------: | :--------------------: | :---------: |:---------: |
+|    --     |       Listwise       |       --        |     --         |
diff --git a/setup.py b/setup.py
index 31bb34f03187dc9ab29c4cc5c75c559540ca8269..f64ae5cf358e04feec2226b3bd9ae23c89234b53 100644
--- a/setup.py
+++ b/setup.py
@@ -62,8 +62,9 @@ def build(dirname):
 
     models_copy = [
         'data/*.txt', 'data/*/*.txt', '*.yaml', '*.sh', 'tree/*.npy',
-        'tree/*.txt'
+        'tree/*.txt', 'data/sample_data/*', 'data/sample_data/train/*'
     ]
+
     engine_copy = ['*/*.sh']
     for package in packages:
         if package.startswith("paddlerec.models."):