layoutlmv3 base chinese

Introduction

LayoutLMv3 is a pre-trained multimodal Transformer model designed for Document AI tasks, developed by Microsoft. It combines unified text and image masking to handle various document-related tasks effectively. LayoutLMv3 can be fine-tuned for both text-centric tasks like form understanding and document visual question answering, as well as image-centric tasks like document image classification and layout analysis.

Architecture

The model employs a simple yet powerful unified architecture and training objectives, allowing it to serve as a general-purpose pre-trained model. This design is particularly beneficial for tasks requiring the integration of textual and visual data.

Training

LayoutLMv3 is pre-trained using a combination of text and image data, leveraging the unified masking approach to enhance its capability in understanding and processing document layouts and content. The training details and results are available in the preprint paper by Yupan Huang et al., titled "LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking."

Guide: Running Locally

1. Environment Setup:

   • Ensure you have Python and PyTorch installed.
   • Install the Hugging Face Transformers library:

     pip install transformers
     

2. Model Download:

   • Access the model from Hugging Face's model hub using the model name microsoft/layoutlmv3-base-chinese.

3. Inference:

   • Use the model for document AI tasks by loading it in a Python script and applying it to your data.

4. Cloud GPUs:

   • For enhanced performance, consider using cloud GPU services like AWS EC2, Google Cloud, or Azure.

License

The project is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Portions of the source code are based on the Hugging Face Transformers project. For more details, refer to the Creative Commons license.