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 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.
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.
The model is meant to be fine-tuned on tasks like document image classification, document parsing and document QA. See the
model hub to look for fine-tuned versions on a task that interests you.
For code examples, we refer to the
documentation.
1@misc{https://doi.org/10.48550/arxiv.2202.13669,
2 doi = {10.48550/ARXIV.2202.13669},
3
4 url = {https://arxiv.org/abs/2202.13669},
5
6 author = {Wang, Jiapeng and Jin, Lianwen and Ding, Kai},
7
8 keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
9
10 title = {LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding},
11
12 publisher = {arXiv},
13
14 year = {2022},
15
16 copyright = {arXiv.org perpetual, non-exclusive license}
17}
