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<nowiki>language: en</nowiki> | |||
<nowiki>license: cc-by-nc-sa-4.0</nowiki> | |||
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= LayoutLMv3 = | |||
[https://www.microsoft.com/en-us/research/project/document-ai/ Microsoft Document AI] | [https://aka.ms/layoutlmv3 GitHub] | |||
== Model description == | |||
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis. | |||
[https://arxiv.org/abs/2204.08387 LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking] | |||
Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, ACM Multimedia 2022. | |||
== Citation == | |||
If you find LayoutLM useful in your research, please cite the following paper: | |||
<pre> | |||
@inproceedings{huang2022layoutlmv3, | |||
author={Yupan Huang and Tengchao Lv and Lei Cui and Yutong Lu and Furu Wei}, | |||
title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking}, | |||
booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, | |||
year={2022} | |||
} | |||
</pre> | |||
== License == | |||
The content of this project itself is licensed under the [https://creativecommons.org/licenses/by-nc-sa/4.0/ Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)]. | |||
Portions of the source code are based on the [https://github.com/huggingface/transformers transformers] project. | |||
[https://opensource.microsoft.com/codeofconduct Microsoft Open Source Code of Conduct] | |||