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| <nowiki>---</nowiki>
| | {{Model infobox |
| <nowiki>language: en</nowiki>
| | | hugging-face-uri = openai/clip-vit-large-patch14 |
| <nowiki>license: cc-by-nc-sa-4.0</nowiki>
| | | type = |
| <nowiki>---</nowiki>
| | | task = |
| | | library = |
| | | dataset = |
| | | language = |
| | | paper = |
| | | related-to = |
| | | license = |
| | }} |
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| = LayoutLMv3 = | | {{Model infobox |
| | | hugging-face-uri = bert-base-uncased |
| | | type = |
| | | task = |
| | | library = |
| | | dataset = |
| | | language = |
| | | paper = |
| | | related-to = |
| | | license = |
| | }} |
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| [https://www.microsoft.com/en-us/research/project/document-ai/ Microsoft Document AI] | [https://aka.ms/layoutlmv3 GitHub]
| | </noinclude> |
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| == Model description ==
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| 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.
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| [https://arxiv.org/abs/2204.08387 LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking]
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| Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, ACM Multimedia 2022.
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| == Citation ==
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| If you find LayoutLM useful in your research, please cite the following paper:
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| <pre>
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| @inproceedings{huang2022layoutlmv3,
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| author={Yupan Huang and Tengchao Lv and Lei Cui and Yutong Lu and Furu Wei},
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| title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking},
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| booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
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| year={2022}
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| }
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| </pre> | |
| == License ==
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| 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)].
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| Portions of the source code are based on the [https://github.com/huggingface/transformers transformers] project.
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| [https://opensource.microsoft.com/codeofconduct Microsoft Open Source Code of Conduct]
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