-
Notifications
You must be signed in to change notification settings - Fork 28.2k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
[New Model] UDOP: Unifying Vision, Text, and Layout for Universal Document Processing #20650
Comments
@NielsRogge as you implemented Donut, you might be interested :) |
Let's hope they open-source :) |
@NielsRogge they added the code here /~https://github.com/microsoft/i-Code/tree/main/i-Code-Doc |
Hi @NielsRogge, can I help in this implementation? |
@NielsRogge here you have the weights: https://huggingface.co/ZinengTang/Udop/tree/main |
@WaterKnight1998 Is the model accessible now? |
No, the PR from @raghavanone was closed. @NielsRogge is working on opening a PR with a refactor of UDop code as it was not very good. I saw he has a branch for this: /~https://github.com/NielsRogge/transformers/tree/add_udop |
Model description
We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 9 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark (DUE).
Open source status
Provide useful links for the implementation
UDOP Paper: https://arxiv.org/abs/2212.02623
UDOP Repo: /~https://github.com/microsoft/UDOP
UDOP Model Weights: https://huggingface.co/ZinengTang/Udop/tree/main
The text was updated successfully, but these errors were encountered: