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*This model was released on 2021-04-18 and added to Hugging Face Transformers on 2021-11-03.*

# LayoutXLM

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

LayoutXLM was proposed in [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://huggingface.co/papers/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha
Zhang, Furu Wei. It's a multilingual extension of the [LayoutLMv2 model](https://huggingface.co/papers/2012.14740) trained
on 53 languages.

The abstract from the paper is the following:

*Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document
understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In
this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to
bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also
introduce a multilingual form understanding benchmark dataset named XFUN, which includes form understanding samples in
7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled
for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA
cross-lingual pre-trained models on the XFUN dataset.*

This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm).

## Usage tips and examples

One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like so:

```python
from transformers import LayoutLMv2Model

model = LayoutLMv2Model.from_pretrained("microsoft/layoutxlm-base")
```

Note that LayoutXLM has its own tokenizer, based on
[`LayoutXLMTokenizer`]/[`LayoutXLMTokenizerFast`]. You can initialize it as
follows:

```python
from transformers import LayoutXLMTokenizer

tokenizer = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")
```

Similar to LayoutLMv2, you can use [`LayoutXLMProcessor`] (which internally applies
[`LayoutLMv2ImageProcessor`] and
[`LayoutXLMTokenizer`]/[`LayoutXLMTokenizerFast`] in sequence) to prepare all
data for the model.

<Tip>

As LayoutXLM's architecture is equivalent to that of LayoutLMv2, one can refer to [LayoutLMv2's documentation page](layoutlmv2) for all tips, code examples and notebooks.
</Tip>

## LayoutXLMConfig

[[autodoc]] LayoutXLMConfig

## LayoutXLMTokenizer

[[autodoc]] LayoutXLMTokenizer
    - __call__
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## LayoutXLMTokenizerFast

[[autodoc]] LayoutXLMTokenizerFast
    - __call__

## LayoutXLMProcessor

[[autodoc]] LayoutXLMProcessor
    - __call__
