Instructions to use microsoft/xdoc-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xdoc-base with Transformers:
# Load model directly from transformers import AutoTokenizer, Layoutlmv1ForMaskedLM_roberta tokenizer = AutoTokenizer.from_pretrained("microsoft/xdoc-base") model = Layoutlmv1ForMaskedLM_roberta.from_pretrained("microsoft/xdoc-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| # XDoc | |
| ## Introduction | |
| XDoc is a unified pre-trained model that deals with different document formats in a single model. With only 36.7% parameters, XDoc achieves comparable or better performance on downstream tasks, which is cost-effective for real-world deployment. | |
| [XDoc: Unified Pre-training for Cross-Format Document Understanding](https://arxiv.org/abs/2210.02849) | |
| Jingye Chen, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei, [EMNLP 2022](#) | |
| ## Citation | |
| If you find XDoc helpful, please cite us: | |
| ``` | |
| @article{chen2022xdoc, | |
| title={XDoc: Unified Pre-training for Cross-Format Document Understanding}, | |
| author={Chen, Jingye and Lv, Tengchao and Cui, Lei and Zhang, Cha and Wei, Furu}, | |
| journal={arXiv preprint arXiv:2210.02849}, | |
| year={2022} | |
| } | |
| ``` | |