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README.md

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 ---
 license: mit
+tags:
+- vision
 ---
+
+# LiLT-RoBERTa Multilingual (base-sized model) 
+
+Language-Independent Layout Transformer - RoBERTa model by stitching a pre-trained RoBERTa (English) 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).
+
+
+## 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}
+}
+```