layoutlmv3 large

Introduction

LayoutLMv3 is a pre-trained multimodal Transformer model developed by Microsoft for Document AI. It integrates text and image processing, providing a versatile architecture suitable for various document processing tasks such as form understanding, receipt scanning, document visual question answering, document image classification, and layout analysis.

Architecture

LayoutLMv3 employs a unified architecture that combines text and image masking. This design allows the model to efficiently handle both text-centric and image-centric tasks, making it a general-purpose tool for document-related AI applications.

Training

The model is pre-trained using a combination of text and image data, with masking techniques applied to both modalities. This training approach enhances the model's ability to understand and process documents in a multimodal context, improving accuracy and performance in downstream tasks.

Guide: Running Locally

1. Setup Environment: Ensure you have Python installed along with libraries such as PyTorch and Hugging Face Transformers.
2. Install Dependencies: Use pip to install necessary packages:

   pip install torch torchvision transformers
   

3. Load Model: Use the Transformers library to load LayoutLMv3.

   from transformers import LayoutLMv3ForSequenceClassification, LayoutLMv3Tokenizer
   model = LayoutLMv3ForSequenceClassification.from_pretrained('microsoft/layoutlmv3-large')
   tokenizer = LayoutLMv3Tokenizer.from_pretrained('microsoft/layoutlmv3-large')
   

4. Inference: Prepare your document data and perform inference using the loaded model.

For enhanced performance, especially for large-scale tasks, consider using cloud GPUs through platforms like AWS, Azure, or Google Cloud.

License

LayoutLMv3 is released under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. This allows for sharing and adapting the model non-commercially, as long as appropriate credit is given, and any derivatives are licensed under the same terms. Portions of the source code are based on the Hugging Face Transformers project, and users must adhere to Microsoft's Open Source Code of Conduct.