layoutlmv2 large uncased

Introduction

LayoutLMv2 is an enhanced version of LayoutLM designed for multi-modal document AI, integrating text, layout, and image data into a single framework. It introduces new pre-training tasks to better model the interactions among these elements. The model shows significant improvements over previous baselines, achieving state-of-the-art results in various visually-rich document understanding tasks such as FUNSD, CORD, SROIE, Kleister-NDA, RVL-CDIP, and DocVQA.

Architecture

LayoutLMv2 utilizes a multi-modal architecture that combines text, layout, and image features. This integration allows it to effectively process and understand complex document structures, improving performance on tasks involving visually-rich documents.

Training

The training of LayoutLMv2 involves multi-modal pre-training techniques that focus on the interplay between text, layout, and image data. This approach enhances the model's ability to comprehend and process document elements, leading to superior results in document understanding tasks.

Guide: Running Locally

1. Install Dependencies: Ensure you have Python and PyTorch installed. Use pip to install the necessary libraries from the Hugging Face Transformers library.

   pip install transformers
   

2. Download the Model: Use the Hugging Face Model Hub to download layoutlmv2-large-uncased.

   from transformers import LayoutLMv2ForSequenceClassification, LayoutLMv2Tokenizer
   
   model = LayoutLMv2ForSequenceClassification.from_pretrained("microsoft/layoutlmv2-large-uncased")
   tokenizer = LayoutLMv2Tokenizer.from_pretrained("microsoft/layoutlmv2-large-uncased")
   

3. Prepare Data: Format your document data to include text, layout, and images as required by the model.

4. Inference: Run the model on your data to obtain predictions.

   inputs = tokenizer("Your document text here", return_tensors="pt")
   outputs = model(**inputs)
   

For optimal performance, especially for large datasets or complex documents, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

LayoutLMv2 is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). This license allows for sharing and adaptation for non-commercial purposes, provided appropriate credit is given and adaptations are shared under the same terms.