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README.md
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---
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license: mit
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tags:
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- vision
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---
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# LiLT-InfoXLM (base-sized model)
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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).
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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.
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## Model description
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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.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg" alt="drawing" width="600"/>
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## Intended uses & limitations
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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.
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### How to use
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For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/lilt.html).
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### BibTeX entry and citation info
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```bibtex
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@misc{https://doi.org/10.48550/arxiv.2202.13669,
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doi = {10.48550/ARXIV.2202.13669},
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url = {https://arxiv.org/abs/2202.13669},
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author = {Wang, Jiapeng and Jin, Lianwen and Ding, Kai},
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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title = {LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding},
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publisher = {arXiv},
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year = {2022},
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copyright = {arXiv.org perpetual, non-exclusive license}
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}
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```
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