| license: mit | |
| tags: | |
| - vision | |
| # LiLT-InfoXLM (base-sized model) | |
| Language-Independent Layout Transformer - InfoXLM model by stitching a pre-trained InfoXLM and a pre-trained Language-Independent Layout Transformer (LiLT) together. It was introduced in the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Wang et al. and first released in [this repository](https://github.com/jpwang/lilt). | |
| Disclaimer: The team releasing LiLT did not write a model card for this model so this model card has been written by the Hugging Face team. | |
| ## Model description | |
| The Language-Independent Layout Transformer (LiLT) allows to combine any pre-trained RoBERTa encoder from the hub (hence, in any language) with a lightweight Layout Transformer to have a LayoutLM-like model for any language. | |
| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg" alt="drawing" width="600"/> | |
| ## Intended uses & limitations | |
| The model is meant to be fine-tuned on tasks like document image classification, document parsing and document QA. See the [model hub](https://huggingface.co/models?search=lilt) to look for fine-tuned versions on a task that interests you. | |
| ### How to use | |
| For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/lilt.html). | |
| ### BibTeX entry and citation info | |
| ```bibtex | |
| @misc{https://doi.org/10.48550/arxiv.2202.13669, | |
| doi = {10.48550/ARXIV.2202.13669}, | |
| url = {https://arxiv.org/abs/2202.13669}, | |
| author = {Wang, Jiapeng and Jin, Lianwen and Ding, Kai}, | |
| keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, | |
| title = {LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding}, | |
| publisher = {arXiv}, | |
| year = {2022}, | |
| copyright = {arXiv.org perpetual, non-exclusive license} | |
| } | |
| ``` |