diff --git a/doc/doc_ch/kie.md b/doc/doc_ch/kie.md
index b6f38a662fd98597011c5a51ff29c417d880ca17..26d2e560fce4d5208eb72a033d315d27da1a5577 100644
--- a/doc/doc_ch/kie.md
+++ b/doc/doc_ch/kie.md
@@ -438,7 +438,25 @@ inference/ser_vi_layoutxlm/
└── inference.pdmodel # inference模型的模型结构文件
```
-RE任务的动转静过程适配中,敬请期待。
+信息抽取模型中的RE任务转inference模型步骤如下:
+
+``` bash
+# -c 后面设置训练算法的yml配置文件
+# -o 配置可选参数
+# Architecture.Backbone.checkpoints 参数设置待转换的训练模型地址
+# Global.save_inference_dir 参数设置转换的模型将保存的地址
+
+python3 tools/export_model.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/re_vi_layoutxlm_xfund_zh/best_accuracy Global.save_inference_dir=./inference/re_vi_layoutxlm
+```
+
+转换成功后,在目录下有三个文件:
+
+```
+inference/re_vi_layoutxlm/
+ ├── inference.pdiparams # inference模型的参数文件
+ ├── inference.pdiparams.info # inference模型的参数信息,可忽略
+ └── inference.pdmodel # inference模型的模型结构文件
+```
## 4.2 模型推理
@@ -461,6 +479,26 @@ python3 kie/predict_kie_token_ser.py \
+VI-LayoutXLM模型基于RE任务进行推理,可以执行如下命令:
+
+```bash
+cd ppstructure
+python3 kie/predict_kie_token_ser_re.py \
+ --kie_algorithm=LayoutXLM \
+ --re_model_dir=../inference/re_vi_layoutxlm \
+ --ser_model_dir=../inference/ser_vi_layoutxlm \
+ --use_visual_backbone=False \
+ --image_dir=./docs/kie/input/zh_val_42.jpg \
+ --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
+ --vis_font_path=../doc/fonts/simfang.ttf \
+ --ocr_order_method="tb-yx"
+```
+
+RE可视化结果默认保存到`./output`文件夹里面,结果示例如下:
+
+
+
+
# 5. FAQ
diff --git a/doc/doc_en/kie_en.md b/doc/doc_en/kie_en.md
index 0c335a5ceb8991b80bc0cab6facdf402878abb50..cd1fffb27ac1c2a399a916e1ba5f5c3f87032515 100644
--- a/doc/doc_en/kie_en.md
+++ b/doc/doc_en/kie_en.md
@@ -457,14 +457,31 @@ inference/ser_vi_layoutxlm/
└── inference.pdmodel # The program file of recognition
```
-Export of RE model is also in adaptation.
+The RE model can be converted to the inference model using the following command.
+
+```bash
+# -c Set the training algorithm yml configuration file.
+# -o Set optional parameters.
+# Architecture.Backbone.checkpoints Set the training model address.
+# Global.save_inference_dir Set the address where the converted model will be saved.
+python3 tools/export_model.py -c configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/re_vi_layoutxlm_xfund_zh/best_accuracy Global.save_inference_dir=./inference/re_vi_layoutxlm
+```
+
+After the conversion is successful, there are three files in the model save directory:
+
+```
+inference/re_vi_layoutxlm/
+ ├── inference.pdiparams # The parameter file of recognition inference model
+ ├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
+ └── inference.pdmodel # The program file of recognition
+```
## 4.2 Model inference
The VI layoutxlm model performs reasoning based on the ser task, and can execute the following commands:
-Using the following command to infer the VI-LayoutXLM model.
+Using the following command to infer the VI-LayoutXLM SER model.
```bash
cd ppstructure
@@ -483,6 +500,26 @@ The visualized result will be saved in `./output`, which is shown as follows.
+Using the following command to infer the VI-LayoutXLM RE model.
+
+```bash
+cd ppstructure
+python3 kie/predict_kie_token_ser_re.py \
+ --kie_algorithm=LayoutXLM \
+ --re_model_dir=../inference/re_vi_layoutxlm \
+ --ser_model_dir=../inference/ser_vi_layoutxlm \
+ --use_visual_backbone=False \
+ --image_dir=./docs/kie/input/zh_val_42.jpg \
+ --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
+ --vis_font_path=../doc/fonts/simfang.ttf \
+ --ocr_order_method="tb-yx"
+```
+
+The visualized result will be saved in `./output`, which is shown as follows.
+
+
+
+
# 5. FAQ