fix readme.md and config.yaml

README.md

@@ -56,6 +56,7 @@
     |         Rank          |                  [xDeepFM](models/rank/xdeepfm/model.py)                  |   ✓   |    x    |        ✓         |     x     | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)                                                       |
     |         Rank          |                      [DIN](models/rank/din/model.py)                      |   ✓   |    x    |        ✓         |     x     | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)                                                                                     |
     |   Rank   |                      [DIEN](models/rank/dien/model.py)                      |    ✓    |    x    |     ✓     |     x     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423)                                                                                     |
+    |   Rank   |                      [AutoInt](models/rank/AutoInt/model.py)                      |    ✓    |    x    |     ✓     |     x     | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf)                                                                                     |
     |         Rank          |                [Wide&Deep](models/rank/wide_deep/model.py)                |   ✓   |    x    |        ✓         |     x     | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)                                                                                               |
     |         Rank          |                    [FGCNN](models/rank/fgcnn/model.py)                    |   ✓   |    ✓    |        ✓         |     ✓     | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)                                                                      |
     |         Rank          |                  [Fibinet](models/rank/fibinet/model.py)                  |   ✓   |    ✓    |        ✓         |     ✓     | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf)                                                 |

README_CN.md

@@ -61,6 +61,7 @@
     |   排序   |                  [xDeepFM](models/rank/xdeepfm/model.py)                  |    ✓    |    x    |     ✓     |     x     | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)                                                       |
     |   排序   |                      [DIN](models/rank/din/model.py)                      |    ✓    |    x    |     ✓     |     x     | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)                                                                                     |
     |   排序   |                      [DIEN](models/rank/dien/model.py)                      |    ✓    |    x    |     ✓     |     x     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423)                                                                                     |
+    |   Rank   |                      [AutoInt](models/rank/AutoInt/model.py)                      |    ✓    |    x    |     ✓     |     x     | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf)                                                                                     |
     |   排序   |                [Wide&Deep](models/rank/wide_deep/model.py)                |    ✓    |    x    |     ✓     |     x     | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)                                                                                               |
     |   排序   |                    [FGCNN](models/rank/fgcnn/model.py)                    |    ✓    |    ✓    |     ✓     |     ✓     | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)                                                                      |
     |   排序   |                  [Fibinet](models/rank/fibinet/model.py)                  |    ✓    |    ✓    |     ✓     |     ✓     | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf)                                                 |

models/rank/fibinet/config.yaml

@@ -59,8 +59,8 @@ runner:
   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_checkpoint_path: "increment_model_fibinet" # save checkpoint path
+  save_inference_path: "inference_fibinet" # 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
@@ -75,8 +75,8 @@ runner:
   device: gpu
   save_checkpoint_interval: 1 # 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_checkpoint_path: "increment_model_fibinet" # save checkpoint path
+  save_inference_path: "inference_fibinet" # 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
@@ -87,14 +87,14 @@ runner:
   class: infer
   # device to run training or infer
   device: cpu
-  init_model_path: "increment_model" # load model path
+  init_model_path: "increment_model_fibinet" # load model path
   phases: [phase2]
 
 - name: single_gpu_infer
   class: infer
   # device to run training or infer
   device: gpu
-  init_model_path: "increment_model" # load model path
+  init_model_path: "increment_model_fibinet" # load model path
   phases: [phase2]
 
 # runner will run all the phase in each epoch

models/rank/flen/config.yaml

@@ -57,8 +57,8 @@ runner:
   device: cpu
   save_checkpoint_interval: 1 # 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_checkpoint_path: "increment_model_flen" # save checkpoint path
+  save_inference_path: "inference_flen" # 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
@@ -73,8 +73,8 @@ runner:
   device: gpu
   save_checkpoint_interval: 1 # 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_checkpoint_path: "increment_model_flen" # save checkpoint path
+  save_inference_path: "inference_flen" # 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
@@ -85,14 +85,14 @@ runner:
   class: infer
   # device to run training or infer
   device: cpu
-  init_model_path: "increment_model" # load model path
+  init_model_path: "increment_model_flen" # load model path
   phases: [phase2]
 
 - name: single_gpu_infer
   class: infer
   # device to run training or infer
   device: gpu
-  init_model_path: "increment_model" # load model path
+  init_model_path: "increment_model_flen" # load model path
   phases: [phase2]
 
 # runner will run all the phase in each epoch