Lilt Roberta En Base

🚀 LiLT-RoBERTa (base-sized model)

This is a Language-Independent Layout Transformer - RoBERTa model. It is created by stitching a pre-trained RoBERTa (English) and a pre-trained Language-Independent Layout Transformer (LiLT) together. It can be used for structured document understanding tasks.

🚀 Quick Start

This model is introduced in the paper LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding by Wang et al. and first released in this repository.

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.

✨ Features

The Language-Independent Layout Transformer (LiLT) enables the combination of any pre - trained RoBERTa encoder from the hub (in any language) with a lightweight Layout Transformer, creating a LayoutLM - like model for any language.

📚 Documentation

Intended uses & limitations

The model is designed to be fine - tuned on tasks such as document image classification, document parsing, and document QA. You can check the model hub to find fine - tuned versions for tasks that interest you.

How to use

For code examples, please refer to the documentation.

BibTeX entry and citation info

@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}
}

📄 License

This model is released under the MIT license.