diff --git a/models/rank/fibinet/__init__.py b/models/rank/fibinet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54
--- /dev/null
+++ b/models/rank/fibinet/__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/rank/fibinet/config.yaml b/models/rank/fibinet/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..06dbc745493e00591bc8302f62911ff26f3f80be
--- /dev/null
+++ b/models/rank/fibinet/config.yaml
@@ -0,0 +1,96 @@
+# 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.
+
+# workspace
+workspace: "paddlerec.models.rank.fibinet"
+
+# list of dataset
+dataset:
+- name: dataloader_train # name of dataset to distinguish different datasets
+  batch_size: 2
+  type: DataLoader # or QueueDataset 
+  data_path: "{workspace}/data/sample_data/train"
+  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"
+- name: dataset_train # name of dataset to distinguish different datasets
+  batch_size: 2
+  type: QueueDataset # or DataLoader 
+  data_path: "{workspace}/data/sample_data/train"
+  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"
+- name: dataset_infer # name
+  batch_size: 2
+  type: DataLoader # or QueueDataset
+  data_path: "{workspace}/data/sample_data/train"
+  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"
+
+# hyper parameters of user-defined network
+hyper_parameters:
+  # optimizer config
+  optimizer:
+    class: Adam
+    learning_rate: 0.001
+    strategy: async
+  # user-defined <key, value> pairs
+  sparse_inputs_slots: 27
+  sparse_feature_number: 1000001
+  sparse_feature_dim: 9
+  dense_input_dim: 13
+  bilinear_type: 'all'
+  reduction_ratio: 3
+  dropout_rate: 0.5
+
+# select runner by name
+mode: [single_cpu_train, single_cpu_infer]
+# config of each runner.
+# runner is a kind of paddle training class, which wraps the train/infer process.
+runner:
+- name: single_cpu_train
+  class: train
+  # num of epochs
+  epochs: 4
+  # device to run training or infer
+  device: cpu
+  save_checkpoint_interval: 2 # save model interval of epochs
+  save_inference_interval: 4 # save inference
+  save_checkpoint_path: "increment_model" # save checkpoint path
+  save_inference_path: "inference" # save inference path
+  save_inference_feed_varnames: [] # feed vars of save inference
+  save_inference_fetch_varnames: [] # fetch vars of save inference
+  init_model_path: "" # load model path
+  print_interval: 10
+  phases: [phase1]
+
+- name: single_cpu_infer
+  class: infer
+  # num of epochs
+  epochs: 1
+  # device to run training or infer
+  device: cpu
+  init_model_path: "increment_model" # load model path
+  phases: [phase2]
+
+# runner will run all the phase in each epoch
+phase:
+- name: phase1
+  model: "{workspace}/model.py" # user-defined model
+  dataset_name: dataloader_train # select dataset by name
+  thread_num: 1
+
+- name: phase2
+  model: "{workspace}/model.py" # user-defined model
+  dataset_name: dataset_infer # select dataset by name
+  thread_num: 1
+  
diff --git a/models/rank/fibinet/data/download.sh b/models/rank/fibinet/data/download.sh
new file mode 100644
index 0000000000000000000000000000000000000000..56816f7d6be2227fbddafe49d3e24b1ef585a40c
--- /dev/null
+++ b/models/rank/fibinet/data/download.sh
@@ -0,0 +1,13 @@
+wget --no-check-certificate https://fleet.bj.bcebos.com/ctr_data.tar.gz
+tar -zxvf ctr_data.tar.gz
+mv ./raw_data ./train_data_full
+mkdir train_data && cd train_data
+cp ../train_data_full/part-0 ../train_data_full/part-1 ./ && cd ..
+mv ./test_data ./test_data_full
+mkdir test_data && cd test_data
+cp ../test_data_full/part-220 ./  && cd ..
+echo "Complete data download."
+echo "Full Train data stored in ./train_data_full "
+echo "Full Test data stored in ./test_data_full "
+echo "Rapid Verification train data stored in ./train_data "
+echo "Rapid Verification test data stored in ./test_data "
diff --git a/models/rank/fibinet/data/get_slot_data.py b/models/rank/fibinet/data/get_slot_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..cacdc279bd9877bfe974e6f093ba912972bee876
--- /dev/null
+++ b/models/rank/fibinet/data/get_slot_data.py
@@ -0,0 +1,71 @@
+#   Copyright (c) 2019 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 paddle.fluid.incubate.data_generator as dg
+
+cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+hash_dim_ = 1000001
+continuous_range_ = range(1, 14)
+categorical_range_ = range(14, 40)
+
+
+class CriteoDataset(dg.MultiSlotDataGenerator):
+    """
+    DacDataset: inheritance MultiSlotDataGeneratior, Implement data reading
+    Help document: http://wiki.baidu.com/pages/viewpage.action?pageId=728820675
+    """
+
+    def generate_sample(self, line):
+        """
+        Read the data line by line and process it as a dictionary
+        """
+
+        def reader():
+            """
+            This function needs to be implemented by the user, based on data format
+            """
+            features = line.rstrip('\n').split('\t')
+            dense_feature = []
+            sparse_feature = []
+            for idx in continuous_range_:
+                if features[idx] == "":
+                    dense_feature.append(0.0)
+                else:
+                    dense_feature.append(
+                        (float(features[idx]) - cont_min_[idx - 1]) /
+                        cont_diff_[idx - 1])
+            for idx in categorical_range_:
+                sparse_feature.append(
+                    [hash(str(idx) + features[idx]) % hash_dim_])
+            label = [int(features[0])]
+            process_line = dense_feature, sparse_feature, label
+            feature_name = ["dense_feature"]
+            for idx in categorical_range_:
+                feature_name.append("C" + str(idx - 13))
+            feature_name.append("label")
+            s = "click:" + str(label[0])
+            for i in dense_feature:
+                s += " dense_feature:" + str(i)
+            for i in range(1, 1 + len(categorical_range_)):
+                s += " " + str(i) + ":" + str(sparse_feature[i - 1][0])
+            print(s.strip())
+            yield None
+
+        return reader
+
+
+d = CriteoDataset()
+d.run_from_stdin()
diff --git a/models/rank/fibinet/data/run.sh b/models/rank/fibinet/data/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f2d1fc9210d65e521cb7fa19cadab3ec95d95a31
--- /dev/null
+++ b/models/rank/fibinet/data/run.sh
@@ -0,0 +1,25 @@
+sh download.sh
+
+mkdir slot_train_data_full
+for i in `ls ./train_data_full`
+do
+    cat train_data_full/$i | python get_slot_data.py > slot_train_data_full/$i
+done
+
+mkdir slot_test_data_full
+for i in `ls ./test_data_full`
+do
+    cat test_data_full/$i | python get_slot_data.py > slot_test_data_full/$i
+done
+
+mkdir slot_train_data
+for i in `ls ./train_data`
+do
+    cat train_data/$i | python get_slot_data.py > slot_train_data/$i
+done
+
+mkdir slot_test_data
+for i in `ls ./test_data`
+do
+    cat test_data/$i | python get_slot_data.py > slot_test_data/$i
+done
diff --git a/models/rank/fibinet/data/sample_data/train/sample_train.txt b/models/rank/fibinet/data/sample_data/train/sample_train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..585c535a7c8c0b56383557f8ba1779d59c544b11
--- /dev/null
+++ b/models/rank/fibinet/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
+click:1 dense_feature:0.0 dense_feature:0.932006633499 dense_feature:0.02 dense_feature:0.14 dense_feature:0.0395625 dense_feature:0.328 dense_feature:0.98 dense_feature:0.12 dense_feature:1.886 dense_feature:0.0 dense_feature:1.8 dense_feature:0.0 dense_feature:0.14 1:715353 2:761523 3:432904 4:892267 5:515218 6:948614 7:266726 8:67180 9:27346 10:266081 11:286126 12:789480 13:49621 14:255651 15:47663 16:79797 17:342789 18:616331 19:880474 20:984402 21:242209 22:26235 23:669531 24:26284 25:269955 26:187951
+click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.08 dense_feature:0.06 dense_feature:0.14125 dense_feature:0.076 dense_feature:0.05 dense_feature:0.22 dense_feature:0.208 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.06 1:737395 2:952384 3:511141 4:271077 5:286835 6:948614 7:903547 8:507110 9:27346 10:56047 11:612953 12:747707 13:977426 14:671506 15:158148 16:833738 17:342789 18:427155 19:880474 20:537425 21:916237 22:26235 23:468277 24:676936 25:751788 26:363967
+click:0 dense_feature:0.0 dense_feature:0.124378109453 dense_feature:0.02 dense_feature:0.04 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.08 dense_feature:0.024 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.04 1:210127 2:286436 3:183920 4:507656 5:286835 6:906818 7:199553 8:67180 9:502607 10:708281 11:809876 12:888238 13:375164 14:202774 15:459895 16:475933 17:555571 18:847163 19:26230 20:26229 21:808836 22:191474 23:410878 24:315120 25:26224 26:26223
+click:0 dense_feature:0.1 dense_feature:0.0149253731343 dense_feature:0.34 dense_feature:0.32 dense_feature:0.016421875 dense_feature:0.098 dense_feature:0.04 dense_feature:0.96 dense_feature:0.202 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.32 1:230803 2:817085 3:539110 4:388629 5:286835 6:948614 7:586040 8:67180 9:27346 10:271155 11:176640 12:827381 13:36881 14:202774 15:397299 16:411672 17:342789 18:474060 19:880474 20:984402 21:216871 22:26235 23:761351 24:787115 25:884722 26:904135
+click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.13 dense_feature:0.04 dense_feature:0.246203125 dense_feature:0.108 dense_feature:0.05 dense_feature:0.04 dense_feature:0.03 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:737395 2:64837 3:259267 4:336976 5:515218 6:154084 7:847938 8:67180 9:27346 10:708281 11:776766 12:964800 13:324323 14:873363 15:212708 16:637238 17:681378 18:895034 19:673458 20:984402 21:18600 22:26235 23:410878 24:787115 25:884722 26:355412
+click:0 dense_feature:0.0 dense_feature:0.028192371476 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0245625 dense_feature:0.016 dense_feature:0.04 dense_feature:0.12 dense_feature:0.016 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:554760 3:661483 4:263696 5:938478 6:906818 7:786926 8:67180 9:27346 10:245862 11:668197 12:745676 13:432600 14:413795 15:751427 16:272410 17:342789 18:422136 19:26230 20:26229 21:452501 22:26235 23:51381 24:776636 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:1.95 dense_feature:0.28 dense_feature:0.092828125 dense_feature:0.57 dense_feature:0.06 dense_feature:0.4 dense_feature:0.4 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.4 1:371155 2:817085 3:773609 4:555449 5:938478 6:906818 7:166117 8:507110 9:27346 10:545822 11:316654 12:172765 13:989600 14:255651 15:792372 16:606361 17:342789 18:566554 19:880474 20:984402 21:235256 22:191474 23:700326 24:787115 25:884722 26:569095
+click:0 dense_feature:0.0 dense_feature:0.0912106135987 dense_feature:0.01 dense_feature:0.02 dense_feature:0.06625 dense_feature:0.018 dense_feature:0.05 dense_feature:0.06 dense_feature:0.098 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.04 1:230803 2:531472 3:284417 4:661677 5:938478 6:553107 7:21150 8:49466 9:27346 10:526914 11:164508 12:631773 13:882348 14:873363 15:523948 16:687081 17:342789 18:271301 19:26230 20:26229 21:647160 22:26235 23:410878 24:231695 25:26224 26:26223
+click:1 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.01 dense_feature:0.02 dense_feature:0.02153125 dense_feature:0.092 dense_feature:0.05 dense_feature:0.68 dense_feature:0.472 dense_feature:0.0 dense_feature:0.3 dense_feature:0.0 dense_feature:0.02 1:737395 2:532829 3:320762 4:887282 5:286835 6:25207 7:640357 8:67180 9:27346 10:695831 11:739268 12:835325 13:402539 14:873363 15:125813 16:168896 17:342789 18:374414 19:26230 20:26229 21:850229 22:26235 23:410878 24:480027 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.05 dense_feature:0.04 dense_feature:0.086125 dense_feature:0.098 dense_feature:0.15 dense_feature:0.06 dense_feature:0.228 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.04 1:210127 2:999497 3:646348 4:520638 5:938478 6:906818 7:438398 8:67180 9:27346 10:975902 11:532544 12:708828 13:815045 14:255651 15:896230 16:663630 17:342789 18:820094 19:687226 20:537425 21:481536 22:26235 23:761351 24:888170 25:250729 26:381125
+click:1 dense_feature:0.1 dense_feature:0.00331674958541 dense_feature:0.02 dense_feature:0.02 dense_feature:0.00078125 dense_feature:0.002 dense_feature:0.73 dense_feature:0.08 dense_feature:0.254 dense_feature:0.1 dense_feature:1.4 dense_feature:0.0 dense_feature:0.02 1:715353 2:342833 3:551901 4:73418 5:286835 6:446063 7:219517 8:67180 9:27346 10:668726 11:40711 12:921745 13:361076 14:15048 15:214564 16:400893 17:228085 18:393370 19:26230 20:26229 21:383046 22:26235 23:700326 24:369764 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.142620232172 dense_feature:0.04 dense_feature:0.1 dense_feature:0.08853125 dense_feature:0.028 dense_feature:0.01 dense_feature:0.1 dense_feature:0.028 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.1 1:737395 2:583707 3:519411 4:19103 5:286835 6:906818 7:801403 8:67180 9:27346 10:35743 11:626052 12:142351 13:988058 14:873363 15:617333 16:850339 17:276641 18:696084 19:26230 20:26229 21:121620 22:191474 23:468277 24:18340 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00995024875622 dense_feature:0.0 dense_feature:0.22 dense_feature:0.0251875 dense_feature:0.0 dense_feature:0.0 dense_feature:0.8 dense_feature:0.182 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.84 1:737395 2:19359 3:166075 4:381832 5:286835 6:446063 7:816009 8:67180 9:27346 10:708281 11:619790 12:524128 13:826787 14:202774 15:371495 16:392894 17:644532 18:271180 19:26230 20:26229 21:349978 22:26235 23:761351 24:517170 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.0149253731343 dense_feature:0.52 dense_feature:0.1 dense_feature:6.25153125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.3 dense_feature:0.03 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.1 1:230803 2:24784 3:519411 4:19103 5:843054 6:948614 7:529143 8:67180 9:502607 10:708281 11:430027 12:142351 13:529101 14:202774 15:618316 16:850339 17:644532 18:95370 19:880474 20:31181 21:121620 22:26235 23:744389 24:18340 25:269955 26:683431
+click:0 dense_feature:0.0 dense_feature:0.0480928689884 dense_feature:0.12 dense_feature:0.22 dense_feature:0.541703125 dense_feature:1.062 dense_feature:0.01 dense_feature:0.24 dense_feature:0.054 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.22 1:737395 2:378661 3:21539 4:552097 5:286835 6:553107 7:512138 8:67180 9:27346 10:708281 11:91094 12:516991 13:150114 14:873363 15:450569 16:353024 17:228085 18:539379 19:26230 20:26229 21:410733 22:26235 23:700326 24:272703 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.016583747927 dense_feature:0.06 dense_feature:0.0 dense_feature:0.209625 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.09 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:737395 2:750131 3:807749 4:905739 5:286835 6:906818 7:11935 8:67180 9:27346 10:708281 11:505199 12:285350 13:724106 14:255651 15:625913 16:511836 17:644532 18:102288 19:26230 20:26229 21:726818 22:179327 23:744389 24:176417 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.05 dense_feature:0.14 dense_feature:0.226703125 dense_feature:0.12 dense_feature:0.05 dense_feature:0.14 dense_feature:0.112 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.14 1:736218 2:690313 3:757279 4:763330 5:286835 6:553107 7:89560 8:642551 9:27346 10:128328 11:281593 12:246510 13:200341 14:255651 15:899145 16:807138 17:342789 18:659853 19:26230 20:26229 21:399608 22:26235 23:669531 24:787115 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.3 dense_feature:0.2 dense_feature:0.021296875 dense_feature:0.83 dense_feature:0.2 dense_feature:0.56 dense_feature:1.122 dense_feature:0.0 dense_feature:0.5 dense_feature:0.0 dense_feature:0.2 1:715353 2:283434 3:523722 4:590869 5:286835 6:948614 7:25472 8:67180 9:27346 10:340404 11:811342 12:679454 13:897590 14:813514 15:578769 16:962576 17:342789 18:267210 19:310188 20:537425 21:746185 22:179327 23:761351 24:416923 25:253255 26:249672
+click:1 dense_feature:0.05 dense_feature:0.0149253731343 dense_feature:0.03 dense_feature:0.24 dense_feature:0.0 dense_feature:0.008 dense_feature:0.4 dense_feature:0.62 dense_feature:0.82 dense_feature:0.1 dense_feature:1.4 dense_feature:0.0 dense_feature:0.08 1:715353 2:532829 3:716475 4:940968 5:286835 6:948614 7:38171 8:67180 9:27346 10:619455 11:515541 12:779426 13:711791 14:255651 15:881750 16:408550 17:342789 18:612540 19:26230 20:26229 21:23444 22:26235 23:410878 24:88425 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.11 dense_feature:0.08 dense_feature:0.135265625 dense_feature:0.426 dense_feature:0.06 dense_feature:0.06 dense_feature:0.42 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.08 1:737395 2:817085 3:506158 4:48876 5:286835 6:948614 7:95506 8:67180 9:27346 10:75825 11:220591 12:613471 13:159874 14:255651 15:121379 16:889290 17:681378 18:532453 19:880474 20:537425 21:717912 22:26235 23:270873 24:450199 25:884722 26:382723
+click:0 dense_feature:0.0 dense_feature:0.0829187396352 dense_feature:0.0 dense_feature:0.0 dense_feature:0.555859375 dense_feature:0.318 dense_feature:0.03 dense_feature:0.0 dense_feature:0.02 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:715353 2:465222 3:974451 4:892661 5:938478 6:948614 7:651987 8:67180 9:27346 10:708281 11:229311 12:545057 13:875629 14:149134 15:393524 16:213237 17:681378 18:540092 19:26230 20:26229 21:483290 22:26235 23:700326 24:946673 25:26224 26:26223
+click:1 dense_feature:0.05 dense_feature:0.854063018242 dense_feature:0.01 dense_feature:0.04 dense_feature:0.000171875 dense_feature:0.004 dense_feature:0.01 dense_feature:0.04 dense_feature:0.004 dense_feature:0.1 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:737395 2:99294 3:681584 4:398205 5:914075 6:906818 7:620358 8:67180 9:27346 10:147441 11:364583 12:535262 13:516341 14:813514 15:281303 16:714384 17:276641 18:443922 19:26230 20:26229 21:948746 22:26235 23:700326 24:928903 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.45190625 dense_feature:0.048 dense_feature:0.01 dense_feature:0.16 dense_feature:0.044 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:792512 3:676584 4:995262 5:938478 6:906818 7:888723 8:67180 9:27346 10:708281 11:310529 12:951172 13:885793 14:873363 15:62698 16:672021 17:276641 18:11502 19:880474 20:984402 21:501083 22:191474 23:744389 24:398029 25:218743 26:991064
+click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.51 dense_feature:0.0 dense_feature:0.2689375 dense_feature:0.0 dense_feature:0.0 dense_feature:0.02 dense_feature:0.006 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:230803 2:239052 3:323170 4:474182 5:140103 6:553107 7:757837 8:524745 9:27346 10:743444 11:883533 12:123023 13:621127 14:255651 15:570872 16:883618 17:924903 18:984920 19:964183 20:984402 21:260134 22:179327 23:410878 24:787860 25:269955 26:949924
+click:0 dense_feature:0.0 dense_feature:0.273631840796 dense_feature:0.0 dense_feature:0.0 dense_feature:0.066453125 dense_feature:0.052 dense_feature:0.04 dense_feature:0.06 dense_feature:0.01 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:531472 3:747313 4:362684 5:843054 6:553107 7:863980 8:718499 9:27346 10:881217 11:371751 12:168971 13:290788 14:202774 15:316669 16:269663 17:342789 18:136775 19:26230 20:26229 21:76865 22:26235 23:761351 24:441421 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.116086235489 dense_feature:0.43 dense_feature:0.36 dense_feature:0.000953125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.36 dense_feature:0.036 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.36 1:737395 2:24784 3:677469 4:820784 5:286835 6:553107 7:715520 8:718499 9:27346 10:708281 11:670424 12:122926 13:724619 14:873363 15:845517 16:488791 17:644532 18:183573 19:880474 20:31181 21:46761 22:26235 23:700326 24:629361 25:269955 26:862373
+click:0 dense_feature:2.55 dense_feature:0.0348258706468 dense_feature:0.01 dense_feature:0.38 dense_feature:0.001453125 dense_feature:0.046 dense_feature:1.11 dense_feature:0.44 dense_feature:2.312 dense_feature:0.2 dense_feature:1.1 dense_feature:0.0 dense_feature:0.46 1:594517 2:194636 3:496284 4:323209 5:286835 6:553107 7:259696 8:760861 9:27346 10:698046 11:478868 12:576074 13:635369 14:201966 15:926692 16:972906 17:342789 18:409802 19:26230 20:26229 21:395694 22:26235 23:410878 24:844671 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.144278606965 dense_feature:0.43 dense_feature:0.22 dense_feature:0.00309375 dense_feature:0.15 dense_feature:0.14 dense_feature:0.54 dense_feature:0.152 dense_feature:0.0 dense_feature:0.2 dense_feature:0.1 dense_feature:0.22 1:737395 2:239052 3:456744 4:736474 5:286835 6:948614 7:13277 8:67180 9:27346 10:958384 11:778183 12:497627 13:136915 14:201966 15:757961 16:747483 17:228085 18:984920 19:905920 20:537425 21:472149 22:179327 23:410878 24:709155 25:269955 26:618673
+click:0 dense_feature:0.0 dense_feature:0.0132669983416 dense_feature:0.4 dense_feature:0.3 dense_feature:0.36440625 dense_feature:1.492 dense_feature:0.07 dense_feature:0.3 dense_feature:1.048 dense_feature:0.0 dense_feature:0.3 dense_feature:0.0 dense_feature:0.3 1:737395 2:19959 3:661391 4:748753 5:286835 6:948614 7:848540 8:67180 9:27346 10:708281 11:703964 12:72024 13:336272 14:255651 15:835686 16:703858 17:342789 18:274368 19:26230 20:26229 21:765452 22:26235 23:700326 24:815200 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.0116086235489 dense_feature:0.01 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:210127 2:662691 3:334228 4:857003 5:286835 6:25207 7:280499 8:67180 9:502607 10:708281 11:195094 12:870026 13:783566 14:873363 15:139595 16:214259 17:555571 18:208248 19:880474 20:984402 21:471770 22:26235 23:744389 24:507551 25:383787 26:797121
+click:1 dense_feature:0.0 dense_feature:0.0348258706468 dense_feature:0.03 dense_feature:0.02 dense_feature:0.066140625 dense_feature:0.006 dense_feature:0.17 dense_feature:0.02 dense_feature:0.236 dense_feature:0.0 dense_feature:0.5 dense_feature:0.0 dense_feature:0.02 1:230803 2:999497 3:25361 4:892267 5:286835 6:906818 7:356528 8:67180 9:27346 10:5856 11:157692 12:554754 13:442501 14:255651 15:896230 16:248781 17:342789 18:820094 19:905920 20:984402 21:916436 22:26235 23:669531 24:26284 25:884722 26:187951
+click:0 dense_feature:0.0 dense_feature:4.62852404643 dense_feature:0.07 dense_feature:0.0 dense_feature:0.022671875 dense_feature:0.0 dense_feature:0.01 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:624252 2:344887 3:238747 4:308366 5:286835 6:553107 7:69291 8:67180 9:27346 10:781054 11:258240 12:546906 13:772337 14:873363 15:807640 16:525695 17:276641 18:613203 19:438655 20:984402 21:415123 22:191474 23:700326 24:729290 25:218743 26:953507
+click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.06 dense_feature:0.02 dense_feature:0.06878125 dense_feature:0.044 dense_feature:0.01 dense_feature:0.22 dense_feature:0.044 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.02 1:737395 2:7753 3:871178 4:183530 5:286835 6:906818 7:273988 8:507110 9:27346 10:708281 11:942072 12:775997 13:612590 14:873363 15:669921 16:639940 17:681378 18:421122 19:880474 20:984402 21:410471 22:26235 23:410878 24:228420 25:269955 26:616000
+click:0 dense_feature:0.0 dense_feature:0.212271973466 dense_feature:0.02 dense_feature:0.28 dense_feature:0.113421875 dense_feature:0.06 dense_feature:0.02 dense_feature:0.28 dense_feature:0.194 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.28 1:210127 2:228963 3:692240 4:389834 5:938478 6:948614 7:125690 8:507110 9:27346 10:708281 11:549232 12:308284 13:262461 14:255651 15:629185 16:280660 17:276641 18:886164 19:26230 20:26229 21:367919 22:191474 23:700326 24:520083 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:1.01658374793 dense_feature:0.01 dense_feature:0.02 dense_feature:0.11759375 dense_feature:0.08 dense_feature:0.02 dense_feature:0.02 dense_feature:0.024 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.02 1:230803 2:7753 3:194720 4:831884 5:286835 6:553107 7:620358 8:67180 9:27346 10:843010 11:424144 12:615986 13:516341 14:813514 15:782575 16:775856 17:342789 18:421122 19:880474 20:984402 21:110090 22:191474 23:700326 24:784174 25:269955 26:101161
+click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.59 dense_feature:0.06 dense_feature:0.04321875 dense_feature:0.192 dense_feature:0.02 dense_feature:0.08 dense_feature:0.014 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.06 1:230803 2:532829 3:26258 4:853241 5:938478 6:948614 7:877607 8:67180 9:27346 10:613723 11:246387 12:538673 13:377975 14:873363 15:659013 16:601478 17:681378 18:199271 19:26230 20:26229 21:300137 22:26235 23:410878 24:372458 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.06135986733 dense_feature:0.0 dense_feature:0.0 dense_feature:0.294671875 dense_feature:0.212 dense_feature:0.26 dense_feature:0.0 dense_feature:0.034 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:154478 3:982044 4:501457 5:819883 6:906818 7:445051 8:67180 9:27346 10:976970 11:783630 12:609883 13:358461 14:15048 15:409791 16:756307 17:342789 18:480228 19:26230 20:26229 21:845147 22:26235 23:669531 24:124290 25:26224 26:26223
+click:1 dense_feature:0.05 dense_feature:0.537313432836 dense_feature:0.0 dense_feature:0.02 dense_feature:0.018578125 dense_feature:0.016 dense_feature:0.16 dense_feature:0.22 dense_feature:0.192 dense_feature:0.1 dense_feature:0.3 dense_feature:0.0 dense_feature:0.02 1:737395 2:194636 3:274597 4:418981 5:286835 6:553107 7:553528 8:67180 9:27346 10:901359 11:110700 12:108037 13:915461 14:255651 15:951604 16:421384 17:342789 18:728110 19:26230 20:26229 21:772733 22:191474 23:761351 24:844671 25:26224 26:26223
+click:0 dense_feature:0.1 dense_feature:0.00663349917081 dense_feature:0.16 dense_feature:0.26 dense_feature:0.00509375 dense_feature:0.122 dense_feature:0.03 dense_feature:0.94 dense_feature:0.526 dense_feature:0.1 dense_feature:0.1 dense_feature:0.0 dense_feature:1.1 1:210127 2:344887 3:343793 4:917598 5:286835 6:948614 7:220413 8:67180 9:27346 10:912799 11:370606 12:722621 13:569604 14:255651 15:499545 16:159495 17:342789 18:613203 19:305384 20:984402 21:844602 22:26235 23:410878 24:695516 25:218743 26:729263
+click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.09 dense_feature:0.16 dense_feature:0.11221875 dense_feature:0.51 dense_feature:0.09 dense_feature:0.48 dense_feature:0.088 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.16 1:737395 2:532829 3:579624 4:980109 5:286835 6:948614 7:927736 8:67180 9:27346 10:970644 11:931289 12:377125 13:539272 14:873363 15:555779 16:405069 17:342789 18:701770 19:26230 20:26229 21:201088 22:26235 23:410878 24:113994 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.182421227197 dense_feature:0.01 dense_feature:0.02 dense_feature:0.000109375 dense_feature:0.978 dense_feature:0.01 dense_feature:0.02 dense_feature:0.062 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.02 1:478318 2:158107 3:508317 4:452336 5:286835 6:948614 7:620358 8:67180 9:27346 10:147441 11:364583 12:34025 13:516341 14:873363 15:502825 16:683439 17:681378 18:889198 19:26230 20:26229 21:234451 22:26235 23:700326 24:256238 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.469320066335 dense_feature:0.2 dense_feature:0.2 dense_feature:0.0705 dense_feature:0.102 dense_feature:0.05 dense_feature:0.22 dense_feature:0.194 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.2 1:715353 2:846239 3:573061 4:508181 5:286835 6:553107 7:892443 8:718499 9:27346 10:639370 11:866496 12:791636 13:895012 14:873363 15:362079 16:16082 17:228085 18:994402 19:880474 20:984402 21:35513 22:26235 23:669531 24:520197 25:934391 26:625657
+click:0 dense_feature:0.0 dense_feature:0.0729684908789 dense_feature:0.06 dense_feature:0.04 dense_feature:5.620296875 dense_feature:0.0 dense_feature:0.0 dense_feature:0.06 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.04 1:399845 2:239052 3:334610 4:593315 5:286835 6:948614 7:751495 8:67180 9:502607 10:111048 11:244081 12:115252 13:915518 14:873363 15:817451 16:296052 17:276641 18:984920 19:774721 20:984402 21:930636 22:26235 23:700326 24:975048 25:269955 26:266439
+click:1 dense_feature:0.05 dense_feature:0.0265339966833 dense_feature:0.07 dense_feature:0.22 dense_feature:1.5625e-05 dense_feature:0.008 dense_feature:0.04 dense_feature:0.36 dense_feature:0.088 dense_feature:0.1 dense_feature:0.3 dense_feature:0.0 dense_feature:0.08 1:737395 2:64837 3:534435 4:555449 5:286835 6:25207 7:661236 8:67180 9:27346 10:708281 11:785752 12:47348 13:524553 14:117289 15:776971 16:293528 17:681378 18:102169 19:758208 20:31181 21:27506 22:26235 23:410878 24:787115 25:884722 26:605635
+click:1 dense_feature:0.1 dense_feature:0.0464344941957 dense_feature:0.0 dense_feature:0.04 dense_feature:0.00059375 dense_feature:0.004 dense_feature:0.02 dense_feature:0.04 dense_feature:0.004 dense_feature:0.1 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:230803 2:7753 3:529866 4:437169 5:938478 6:948614 7:17274 8:67180 9:27346 10:461781 11:452641 12:302471 13:49621 14:873363 15:543432 16:858509 17:681378 18:402164 19:880474 20:984402 21:650184 22:191474 23:410878 24:492581 25:269955 26:217228
+click:0 dense_feature:0.55 dense_feature:0.00829187396352 dense_feature:0.03 dense_feature:0.0 dense_feature:0.0014375 dense_feature:0.004 dense_feature:0.36 dense_feature:0.0 dense_feature:0.042 dense_feature:0.1 dense_feature:0.4 dense_feature:0.0 dense_feature:0.0 1:26973 2:817085 3:961160 4:355882 5:843054 6:906818 7:417593 8:67180 9:27346 10:708281 11:402889 12:899379 13:552051 14:202774 15:532679 16:545549 17:342789 18:562805 19:880474 20:31181 21:355920 22:26235 23:700326 24:787115 25:884722 26:115004
+click:1 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.01 dense_feature:0.02 dense_feature:0.089296875 dense_feature:0.362 dense_feature:0.23 dense_feature:0.04 dense_feature:0.338 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.02 1:230803 2:977337 3:853759 4:880273 5:515218 6:25207 7:414263 8:437731 9:27346 10:205124 11:108170 12:676869 13:388798 14:255651 15:247232 16:172895 17:228085 18:543219 19:26230 20:26229 21:860937 22:179327 23:669531 24:959959 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.0945273631841 dense_feature:0.62 dense_feature:0.24 dense_feature:0.11840625 dense_feature:0.368 dense_feature:0.07 dense_feature:0.24 dense_feature:0.144 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.48 1:737395 2:532829 3:805087 4:186661 5:286835 6:154084 7:468059 8:718499 9:27346 10:708281 11:968875 12:8177 13:47822 14:255651 15:979316 16:956543 17:342789 18:541633 19:26230 20:26229 21:646669 22:26235 23:410878 24:184909 25:26224 26:26223
+click:0 dense_feature:0.3 dense_feature:0.00497512437811 dense_feature:0.12 dense_feature:0.12 dense_feature:0.002890625 dense_feature:0.074 dense_feature:0.06 dense_feature:0.14 dense_feature:0.074 dense_feature:0.1 dense_feature:0.1 dense_feature:0.0 dense_feature:0.74 1:737395 2:64837 3:967865 4:249418 5:938478 6:948614 7:228716 8:67180 9:27346 10:627362 11:722606 12:193782 13:348283 14:255651 15:928582 16:221557 17:342789 18:895034 19:384556 20:984402 21:475712 22:26235 23:410878 24:492875 25:884722 26:468964
+click:0 dense_feature:0.0 dense_feature:0.177446102819 dense_feature:0.01 dense_feature:0.02 dense_feature:0.041859375 dense_feature:0.0 dense_feature:0.0 dense_feature:0.16 dense_feature:0.036 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.02 1:154064 2:834620 3:25206 4:25205 5:938478 6:948614 7:134101 8:92608 9:27346 10:708281 11:505199 12:25711 13:724106 14:671506 15:42927 16:25723 17:644532 18:1957 19:26230 20:26229 21:26236 22:26235 23:744389 24:26233 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:5.61691542289 dense_feature:0.0 dense_feature:0.1 dense_feature:0.043796875 dense_feature:0.302 dense_feature:0.13 dense_feature:0.22 dense_feature:0.3 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.22 1:154184 2:19359 3:166075 4:381832 5:286835 6:906818 7:348227 8:49466 9:27346 10:645596 11:951584 12:524128 13:277250 14:255651 15:853732 16:392894 17:342789 18:619939 19:26230 20:26229 21:349978 22:26235 23:700326 24:517170 25:26224 26:26223
+click:1 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.093234375 dense_feature:0.022 dense_feature:0.04 dense_feature:0.02 dense_feature:0.02 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.0 1:715353 2:485136 3:386313 4:208181 5:286835 6:25207 7:227715 8:49466 9:27346 10:437476 11:733250 12:721260 13:389832 14:255651 15:47178 16:761962 17:342789 18:813169 19:26230 20:26229 21:464938 22:26235 23:410878 24:833196 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.134328358209 dense_feature:0.0 dense_feature:0.14 dense_feature:0.00015625 dense_feature:0.0 dense_feature:0.0 dense_feature:0.14 dense_feature:0.014 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.14 1:737395 2:488655 3:221719 4:442408 5:286835 6:25207 7:898902 8:718499 9:27346 10:457066 11:290973 12:533168 13:949027 14:873363 15:270294 16:934635 17:924903 18:763017 19:880474 20:31181 21:517486 22:26235 23:410878 24:588215 25:499868 26:980179
+click:1 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.023578125 dense_feature:0.0 dense_feature:0.04 dense_feature:0.0 dense_feature:0.046 dense_feature:0.0 dense_feature:0.3 dense_feature:0.0 dense_feature:0.0 1:737395 2:729012 3:691820 4:351286 5:938478 6:553107 7:21150 8:67180 9:27346 10:947459 11:164508 12:205079 13:882348 14:255651 15:178324 16:282716 17:342789 18:193902 19:880474 20:31181 21:604480 22:191474 23:669531 24:727223 25:499868 26:236426
+click:1 dense_feature:0.1 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.00859375 dense_feature:0.006 dense_feature:1.55 dense_feature:0.16 dense_feature:0.06 dense_feature:0.2 dense_feature:1.6 dense_feature:0.0 dense_feature:0.0 1:712372 2:235347 3:483718 4:382039 5:914075 6:906818 7:727609 8:154004 9:27346 10:116648 11:40711 12:658199 13:361076 14:15048 15:15058 16:644988 17:342789 18:544170 19:26230 20:26229 21:251535 22:26235 23:700326 24:114111 25:26224 26:26223
+click:1 dense_feature:0.25 dense_feature:0.192371475954 dense_feature:0.06 dense_feature:0.36 dense_feature:0.0 dense_feature:0.02 dense_feature:0.09 dense_feature:0.42 dense_feature:0.042 dense_feature:0.2 dense_feature:0.3 dense_feature:0.3 dense_feature:0.0 1:737395 2:288975 3:885137 4:368487 5:515218 6:906818 7:569753 8:799133 9:27346 10:635043 11:883202 12:780104 13:492605 14:873363 15:234451 16:94894 17:796504 18:653705 19:880474 20:984402 21:400692 22:26235 23:410878 24:767424 25:934391 26:958132
+click:1 dense_feature:0.15 dense_feature:0.0398009950249 dense_feature:0.02 dense_feature:0.04 dense_feature:1.5625e-05 dense_feature:0.0 dense_feature:0.06 dense_feature:0.04 dense_feature:0.026 dense_feature:0.1 dense_feature:0.3 dense_feature:0.0 dense_feature:0.0 1:715353 2:532829 3:721632 4:377785 5:286835 6:553107 7:959856 8:718499 9:27346 10:737746 11:432444 12:706936 13:169268 14:873363 15:896219 16:461005 17:342789 18:286597 19:26230 20:26229 21:602049 22:26235 23:700326 24:510447 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.05 dense_feature:0.08 dense_feature:0.155421875 dense_feature:0.55 dense_feature:0.08 dense_feature:0.24 dense_feature:1.73 dense_feature:0.0 dense_feature:0.3 dense_feature:0.0 dense_feature:0.08 1:737395 2:288975 3:385122 4:57409 5:286835 6:25207 7:339181 8:67180 9:27346 10:284863 11:531306 12:229544 13:32168 14:117289 15:632422 16:615549 17:342789 18:240865 19:880474 20:984402 21:253725 22:26235 23:410878 24:837371 25:934391 26:948190
+click:0 dense_feature:0.0 dense_feature:0.0398009950249 dense_feature:0.06 dense_feature:0.12 dense_feature:0.11359375 dense_feature:0.55 dense_feature:0.03 dense_feature:0.12 dense_feature:0.186 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.12 1:737395 2:158107 3:738359 4:343895 5:286835 6:948614 7:513189 8:760861 9:27346 10:741641 11:214926 12:142871 13:753229 14:873363 15:502825 16:864586 17:681378 18:889198 19:26230 20:26229 21:368414 22:191474 23:410878 24:256238 25:26224 26:26223
+click:1 dense_feature:0.25 dense_feature:0.00663349917081 dense_feature:0.03 dense_feature:0.04 dense_feature:7.8125e-05 dense_feature:0.0 dense_feature:0.48 dense_feature:0.06 dense_feature:0.004 dense_feature:0.2 dense_feature:1.3 dense_feature:0.0 dense_feature:0.0 1:737395 2:414770 3:100889 4:981572 5:286835 6:446063 7:600430 8:507110 9:27346 10:566014 11:40711 12:330691 13:361076 14:15048 15:176957 16:759140 17:342789 18:212244 19:26230 20:26229 21:688637 22:26235 23:634287 24:762432 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.04 dense_feature:0.02 dense_feature:0.109765625 dense_feature:0.202 dense_feature:0.13 dense_feature:0.02 dense_feature:0.078 dense_feature:0.0 dense_feature:0.1 dense_feature:0.1 dense_feature:0.02 1:737395 2:7753 3:871178 4:183530 5:286835 6:948614 7:358953 8:718499 9:27346 10:837400 11:432444 12:775997 13:169268 14:255651 15:250644 16:639940 17:342789 18:421122 19:880474 20:984402 21:410471 22:26235 23:410878 24:228420 25:269955 26:870795
+click:0 dense_feature:0.05 dense_feature:0.162520729685 dense_feature:0.28 dense_feature:0.16 dense_feature:0.001046875 dense_feature:0.028 dense_feature:1.03 dense_feature:0.84 dense_feature:0.534 dense_feature:0.1 dense_feature:2.3 dense_feature:0.0 dense_feature:0.28 1:737395 2:334074 3:108983 4:898979 5:286835 6:948614 7:600430 8:718499 9:27346 10:668726 11:40711 12:62821 13:361076 14:202774 15:722413 16:688170 17:342789 18:746785 19:957809 20:984402 21:96056 22:191474 23:410878 24:703372 25:129305 26:591537
+click:0 dense_feature:0.2 dense_feature:0.0945273631841 dense_feature:0.02 dense_feature:0.18 dense_feature:0.021078125 dense_feature:0.046 dense_feature:0.52 dense_feature:0.44 dense_feature:0.18 dense_feature:0.1 dense_feature:0.8 dense_feature:0.0 dense_feature:0.22 1:663372 2:532829 3:714247 4:673800 5:286835 6:906818 7:219517 8:67180 9:27346 10:161916 11:40711 12:441505 13:361076 14:255651 15:992961 16:137571 17:796504 18:395194 19:26230 20:26229 21:800938 22:179327 23:410878 24:719782 25:26224 26:26223
+click:1 dense_feature:0.15 dense_feature:0.24543946932 dense_feature:0.0 dense_feature:0.12 dense_feature:0.0001875 dense_feature:0.004 dense_feature:0.08 dense_feature:0.12 dense_feature:0.072 dense_feature:0.1 dense_feature:0.4 dense_feature:0.0 dense_feature:0.04 1:663372 2:70321 3:202829 4:415480 5:286835 6:553107 7:32934 8:67180 9:27346 10:1873 11:699999 12:55775 13:371214 14:873363 15:685332 16:719499 17:342789 18:135819 19:26230 20:26229 21:973542 22:852086 23:410878 24:635223 25:26224 26:26223
+click:0 dense_feature:0.0 dense_feature:0.0679933665008 dense_feature:0.02 dense_feature:0.02 dense_feature:0.20015625 dense_feature:0.016 dense_feature:0.03 dense_feature:0.02 dense_feature:0.014 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.02 1:737395 2:229199 3:956202 4:475901 5:286835 6:948614 7:614385 8:718499 9:27346 10:171202 11:670646 12:566018 13:386065 14:873363 15:936716 16:825279 17:681378 18:758631 19:26230 20:26229 21:113534 22:26235 23:410878 24:551443 25:26224 26:26223
+click:1 dense_feature:0.05 dense_feature:0.00497512437811 dense_feature:0.04 dense_feature:0.22 dense_feature:0.015921875 dense_feature:0.022 dense_feature:0.04 dense_feature:0.4 dense_feature:0.182 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.22 1:737395 2:64837 3:751736 4:291977 5:286835 6:25207 7:377931 8:718499 9:27346 10:724396 11:433484 12:517940 13:439712 14:201966 15:628624 16:780717 17:342789 18:895034 19:880474 20:31181 21:463725 22:26235 23:410878 24:787115 25:884722 26:164940
+click:1 dense_feature:0.0 dense_feature:0.00995024875622 dense_feature:0.15 dense_feature:0.48 dense_feature:0.051375 dense_feature:0.0 dense_feature:0.0 dense_feature:0.06 dense_feature:0.556 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.5 1:737395 2:532829 3:158777 4:112926 5:286835 6:948614 7:764249 8:67180 9:27346 10:795273 11:330644 12:524443 13:78129 14:873363 15:127209 16:146094 17:342789 18:976129 19:26230 20:26229 21:901094 22:26235 23:410878 24:259263 25:26224 26:26223
+click:1 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:1.75 dense_feature:0.0 dense_feature:0.922828125 dense_feature:1.078 dense_feature:0.0 dense_feature:0.0 dense_feature:0.112 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:26973 2:62956 3:428206 4:935291 5:286835 6:446063 7:360307 8:437731 9:502607 10:957425 11:626052 12:641189 13:988058 14:217110 15:637914 16:293992 17:342789 18:832710 19:774721 20:537425 21:516798 22:191474 23:700326 24:204648 25:884722 26:776972
+click:1 dense_feature:1.95 dense_feature:0.00829187396352 dense_feature:0.08 dense_feature:0.1 dense_feature:0.01878125 dense_feature:0.044 dense_feature:0.42 dense_feature:0.24 dense_feature:0.358 dense_feature:0.1 dense_feature:0.2 dense_feature:0.1 dense_feature:0.26 1:737395 2:638265 3:526671 4:362576 5:938478 6:948614 7:999918 8:67180 9:27346 10:806276 11:181589 12:688684 13:367155 14:255651 15:709602 16:386859 17:228085 18:204112 19:668832 20:537425 21:541553 22:191474 23:410878 24:606704 25:49230 26:68113
+click:0 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.38159375 dense_feature:0.022 dense_feature:0.18 dense_feature:0.0 dense_feature:0.016 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:841163 3:284187 4:385559 5:286835 6:446063 7:311604 8:67180 9:27346 10:38910 11:76230 12:520869 13:429321 14:255651 15:296507 16:542357 17:342789 18:377250 19:880474 20:31181 21:325494 22:26235 23:410878 24:26284 25:499868 26:467348
+click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.08 dense_feature:0.0 dense_feature:0.077125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.03 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:737395 2:238813 3:821667 4:209184 5:286835 6:906818 7:261420 8:67180 9:27346 10:748867 11:277196 12:790086 13:495408 14:873363 15:572266 16:281532 17:342789 18:99340 19:880474 20:537425 21:815896 22:26235 23:669531 24:17430 25:734238 26:251811
+click:0 dense_feature:0.0 dense_feature:0.210613598673 dense_feature:0.01 dense_feature:0.0 dense_feature:0.041375 dense_feature:0.0 dense_feature:0.0 dense_feature:0.08 dense_feature:0.026 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:737395 2:532829 3:559456 4:565823 5:286835 6:948614 7:48897 8:67180 9:27346 10:708281 11:214000 12:431427 13:477774 14:873363 15:637383 16:678446 17:276641 18:849284 19:26230 20:26229 21:758879 22:26235 23:410878 24:399458 25:26224 26:26223
+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/fibinet/model.py b/models/rank/fibinet/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..9722a8d36357d487a939f60c2c53ac57cc4123b6
--- /dev/null
+++ b/models/rank/fibinet/model.py
@@ -0,0 +1,159 @@
+# 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 paddle.fluid as fluid
+import itertools
+from paddlerec.core.utils import envs
+from paddlerec.core.model import ModelBase
+
+
+class Model(ModelBase):
+    def __init__(self, config):
+        ModelBase.__init__(self, config)
+
+    def _init_hyper_parameters(self):
+        self.is_distributed = True if envs.get_fleet_mode().upper(
+        ) == "PSLIB" else False
+        self.sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number")
+        self.sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim")
+        self.learning_rate = envs.get_global_env(
+            "hyper_parameters.optimizer.learning_rate")
+
+    def _SENETLayer(self, inputs, filed_size, reduction_ratio=3):
+        reduction_size = max(1, filed_size // reduction_ratio)
+        Z = fluid.layers.reduce_mean(inputs, dim=-1)
+
+        A_1 = fluid.layers.fc(
+            input=Z,
+            size=reduction_size,
+            param_attr=fluid.initializer.Xavier(uniform=False),
+            act='relu',
+            name='W_1')
+
+        A_2 = fluid.layers.fc(
+            input=A_1,
+            size=filed_size,
+            param_attr=fluid.initializer.Xavier(uniform=False),
+            act='relu',
+            name='W_2')
+
+        V = fluid.layers.elementwise_mul(
+            inputs, y=fluid.layers.unsqueeze(
+                input=A_2, axes=[2]))
+
+        return fluid.layers.split(V, num_or_sections=filed_size, dim=1)
+
+    def _BilinearInteraction(self,
+                             inputs,
+                             filed_size,
+                             embedding_size,
+                             bilinear_type="interaction"):
+        if bilinear_type == "all":
+            p = [
+                fluid.layers.elementwise_mul(
+                    fluid.layers.fc(
+                        input=v_i,
+                        size=embedding_size,
+                        param_attr=fluid.initializer.Xavier(uniform=False),
+                        act=None,
+                        name=None),
+                    fluid.layers.squeeze(
+                        input=v_j, axes=[1]))
+                for v_i, v_j in itertools.combinations(inputs, 2)
+            ]
+        else:
+            raise NotImplementedError
+
+        return fluid.layers.concat(input=p, axis=1)
+
+    def _DNNLayer(self, inputs, dropout_rate=0.5):
+        deep_input = inputs
+        for i, hidden_unit in enumerate([400, 400, 400]):
+            fc_out = fluid.layers.fc(
+                input=deep_input,
+                size=hidden_unit,
+                param_attr=fluid.initializer.Xavier(uniform=False),
+                act='relu',
+                name='d_' + str(i))
+            fc_out = fluid.layers.dropout(fc_out, dropout_prob=dropout_rate)
+            deep_input = fc_out
+
+        return deep_input
+
+    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]
+
+        emb = []
+        for data in self.sparse_inputs:
+            feat_emb = fluid.embedding(
+                input=data,
+                size=[self.sparse_feature_number, self.sparse_feature_dim],
+                param_attr=fluid.ParamAttr(
+                    name='dis_emb',
+                    learning_rate=5,
+                    initializer=fluid.initializer.Xavier(
+                        fan_in=self.sparse_feature_dim,
+                        fan_out=self.sparse_feature_dim)),
+                is_sparse=True)
+            emb.append(feat_emb)
+        concat_emb = fluid.layers.concat(emb, axis=1)
+
+        filed_size = len(self.sparse_inputs)
+        bilinear_type = envs.get_global_env("hyper_parameters.bilinear_type")
+        reduction_ratio = envs.get_global_env(
+            "hyper_parameters.reduction_ratio")
+        dropout_rate = envs.get_global_env("hyper_parameters.dropout_rate")
+
+        senet_output = self._SENETLayer(concat_emb, filed_size,
+                                        reduction_ratio)
+        senet_bilinear_out = self._BilinearInteraction(
+            senet_output, filed_size, self.sparse_feature_dim, bilinear_type)
+
+        concat_emb = fluid.layers.split(
+            concat_emb, num_or_sections=filed_size, dim=1)
+        bilinear_out = self._BilinearInteraction(
+            concat_emb, filed_size, self.sparse_feature_dim, bilinear_type)
+        dnn_input = fluid.layers.concat(
+            input=[senet_bilinear_out, bilinear_out, self.dense_input], axis=1)
+        dnn_output = self._DNNLayer(dnn_input, dropout_rate)
+
+        y_pred = fluid.layers.fc(
+            input=dnn_output,
+            size=1,
+            param_attr=fluid.initializer.Xavier(uniform=False),
+            act='sigmoid',
+            name='logit')
+        self.predict = y_pred
+        auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
+                                             label=self.label_input,
+                                             num_thresholds=2**12,
+                                             slide_steps=20)
+
+        if is_infer:
+            self._infer_results["AUC"] = auc
+            self._infer_results["BATCH_AUC"] = batch_auc
+            return
+
+        self._metrics["AUC"] = auc
+        self._metrics["BATCH_AUC"] = batch_auc
+        cost = fluid.layers.log_loss(
+            input=self.predict,
+            label=fluid.layers.cast(
+                x=self.label_input, dtype='float32'))
+        avg_cost = fluid.layers.reduce_mean(cost)
+        self._cost = avg_cost