Multimodal (text + layout/format + image) pre-training for Document AI

LayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. It outperforms strong baselines and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including , including FUNSD (0.7895 → 0.8420), CORD (0.9493 → 0.9601), SROIE (0.9524 → 0.9781), Kleister-NDA (0.834 → 0.852), RVL-CDIP (0.9443 → 0.9564), and DocVQA (0.7295 → 0.8672).

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou, ACL 2021

layoutlmv2-base-uncased | HuggingFace

Please refer to layoutlmft

cd layoutlmft
python -m torch.distributed.launch --nproc_per_node=4 examples/run_funsd.py \
        --model_name_or_path microsoft/layoutlmv2-base-uncased \
        --output_dir /tmp/test-ner \
        --do_train \
        --do_predict \
        --max_steps 1000 \
        --warmup_ratio 0.1 \
        --fp16

If you find LayoutLMv2 useful in your research, please cite the following paper:

@inproceedings{Xu2020LayoutLMv2MP,
  title     = {LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding},
  author    = {Yang Xu and Yiheng Xu and Tengchao Lv and Lei Cui and Furu Wei and Guoxin Wang and Yijuan Lu and Dinei Florencio and Cha Zhang and Wanxiang Che and Min Zhang and Lidong Zhou},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL) 2021},
  year      = {2021}
}

The content of this project itself is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

For help or issues using LayoutLMv2, please submit a GitHub issue.

For other communications related to LayoutLMv2, please contact Lei Cui (lecu@microsoft.com), Furu Wei (fuwei@microsoft.com).