layoutlm_CORD:在 CORD 数据集上评估 Layoutlm 模型

# layoutlm_CORD # Introduction This repo is a implementation of the Layoutlm Model, see [1], from the sourcecode (as I didn't manage to make it work with the huggingface implementation : [HuggingFace Implementation](https://huggingface.co/transformers/model_doc/layoutlm.html) and benchmarked on the CORD Dataset, see [2]. # Results I compare the performance of the pre-train LayoutLM on IIT-CDIP dataset (version LARGE) with the Bert (version Large). ## Validation Set Model | F1_Score | Precision | Recall --- | --- | --- | --- | LayoutLM Large| **0.9562** | **0.9577** | **0.9546** | Bert Large | **0.9474** | **0.9466** | **0.9481** | ## Test Set Model | F1_Score | Precision | Recall --- | --- | --- | --- | LayoutLM Large| **0.9843** | **0.9845** | **0.9841** | Bert Large | **0.9859** | **0.9861** | **0.9856** | In the validation set, Layoutlm outperformed Bert, but it is not the case in the test set. I need to do more investigation. \ Nevertheless, it took Bert 11 minutes to finish the training (4 epochs) while Layoutlm needed only 3 minutes. (same environment, setup ..) # Important files I am using the Layoutlm Large, files of the pre-trained model can be found on these links : \ [OneDrive](https://1drv.ms/u/s!ApPZx_TWwibInSy2nj7YabBsTWNa?e=p4LQo1) / [GoogleDrive](https://drive.google.com/open?id=1tatUuWVuNUxsP02smZCbB5NspyGo7g2g) \ Other ressources can be found on the original repository : [Official Layoutlm](https://github.com/microsoft/unilm/tree/master/layoutlm) ##TODO I will soon put a script for the training, otherwise you can always check my notebooks. \ I will also give more details about the dataset, the notebook's structure ... ## References [1] Yiheng Xu and Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou (2019) , LayoutLM: Pre-training of Text and Layout for Document Image Understanding (https://arxiv.org/abs/1912.13318), https://github.com/microsoft/unilm/tree/master/layoutlm [2] Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk (2019) CORD: A Consolidated Receipt Dataset for Post-OCR Parsing (Document Intelligence Workshop at Neural Information Processing Systems) https://github.com/clovaai/cord