layoutlmv3 base

Introduction

LayoutLMv3 is a pre-trained multimodal Transformer model designed for Document AI, developed by Microsoft. It features a unified architecture for both text and image masking, enabling it to handle a variety of document-related tasks such as form understanding, receipt comprehension, and document image classification.

Architecture

The model employs a simple, unified architecture that integrates text and image information. This design allows it to be versatile for different tasks, whether text-centric or image-centric. The unified text and image masking technique enhances its ability to interpret and analyze documents effectively.

Training

LayoutLMv3 is trained with specific objectives that focus on both text and image data. The pre-training process equips the model to be a general-purpose tool that can be fine-tuned for specific document AI tasks, enabling it to perform tasks such as document visual question answering and document layout analysis.

Guide: Running Locally

To run LayoutLMv3 locally, follow these steps:

1. Installation: Ensure you have Python and PyTorch installed. You can install the Hugging Face Transformers library using pip:

   pip install transformers
   

2. Model Loading: Load the LayoutLMv3 model using the Transformers library:

   from transformers import LayoutLMv3Model
   
   model = LayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base")
   

3. Fine-tuning: Fine-tune the model on your specific dataset to tailor it for tasks like document classification or form understanding.

4. Running on Cloud GPUs: For computational efficiency and faster training times, consider using cloud GPU services such as AWS, Google Cloud, or Azure.

License

LayoutLMv3 is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Portions of the source code are derived from the Hugging Face Transformers project. For detailed terms, refer to the Creative Commons license.