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@ -45,7 +45,7 @@ VI-LayoutXLM基于LayoutXLM进行改进,在下游任务训练过程中,去
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### 4.1 Python推理
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-SER
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- SER
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首先将训练得到的模型转换成inference model。以VI-LayoutXLM模型在XFUND_zh数据集上训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)),可以使用下面的命令进行转换。
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@ -74,7 +74,7 @@ SER可视化结果默认保存到`./output`文件夹里面,结果示例如下
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<img src="../../ppstructure/docs/kie/result_ser/zh_val_42_ser.jpg" width="800">
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</div>
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-RE
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- RE
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首先将训练得到的模型转换成inference model。以VI-LayoutXLM模型在XFUND_zh数据集上训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)),可以使用下面的命令进行转换。
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@ -37,7 +37,7 @@ Please refer to [KIE tutorial](./kie_en.md)。PaddleOCR has modularized the code
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### 4.1 Python Inference
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-SER
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- SER
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First, we need to export the trained model into inference model. Take VI-LayoutXLM model trained on XFUND_zh as an example ([trained model download link](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar)). Use the following command to export.
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@ -70,7 +70,7 @@ The SER visualization results are saved in the `./output` folder by default. The
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</div>
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-RE
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- RE
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First, we need to export the trained model into inference model. Take VI-LayoutXLM model trained on XFUND_zh as an example ([trained model download link](https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar)). Use the following command to export.
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@ -219,7 +219,7 @@ wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layou
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cd ..
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```
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-SER
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- SER
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Use the following command for inference.
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@ -237,7 +237,7 @@ python3 kie/predict_kie_token_ser.py \
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The visual results and text file will be saved in directory `output`.
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-RE
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- RE
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Use the following command for inference.
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@ -203,7 +203,7 @@ wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layou
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cd ..
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```
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-SER
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- SER
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执行下面的命令进行预测。
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@ -220,7 +220,7 @@ python3 kie/predict_kie_token_ser.py \
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可视化结果保存在`output`目录下。
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-RE
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- RE
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执行下面的命令进行预测。
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