# microsoft /layoutlmv3-base-chinese

2.3 kB
 --- language: zh license: cc-by-nc-sa-4.0 --- # LayoutLMv3 [Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3) ## 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. [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, Preprint 2022. ## Results | Dataset | Language | Precision | Recall | F1 | |---------|-----------|------------|------|--------| | [XFUND](https://github.com/doc-analysis/XFUND) | ZH | 0.8980 | 0.9435 | 0.9202 | | Dataset | Subject | Test Time | Name | School | Examination Number | Seat Number | Class | Student Number | Grade | Score | **Mean** | |---------|:------------|:------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [EPHOIE](https://github.com/HCIILAB/EPHOIE) | 98.99 | 100.0 | 99.77 | 99.2 | 100.0 | 100.0 | 98.82 | 99.78 | 98.31 | 97.27 | 99.21 | ## Citation If you find LayoutLM useful in your research, please cite the following paper:  @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} }  ## License The content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Portions of the source code are based on the [transformers](https://github.com/huggingface/transformers) project. [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)