layoutxlm base

Introduction

LayoutXLM is a multimodal pre-trained model designed for multilingual document understanding. It is specifically tailored to handle visually-rich documents across different languages. The model has demonstrated superior performance compared to existing state-of-the-art cross-lingual pre-trained models, particularly when tested on the XFUND dataset.

Architecture

The LayoutXLM model builds on the architecture of LayoutLMv2, incorporating a combination of text, layout/format, and image data to enhance document AI capabilities. This integration allows the model to effectively process and understand documents with complex layouts and multiple languages.

Training

LayoutXLM is trained using multimodal pre-training techniques, which involve leveraging large datasets of multilingual, visually-rich documents. The training process is aimed at improving the model's ability to comprehend and process documents that contain both textual and visual information.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and PyTorch installed. Clone the LayoutXLM repository from GitHub.
2. Install Dependencies: Use pip to install the required libraries, including the Transformers library from Hugging Face.
3. Download Model: Access the LayoutXLM model from Hugging Face's model hub and load it into your environment.
4. Run Inference: Prepare your document data and run it through the model to get predictions.
5. GPU Recommendation: For optimal performance, especially with large document datasets, consider using cloud GPU services such as AWS EC2, Google Cloud Platform, or Azure.

License

LayoutXLM is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (cc-by-nc-sa-4.0) license, which permits sharing and adaptation with attribution, but not for commercial use.