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transformers/docs/source/en/model_doc/layoutxlm.md

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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>
+
+## 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__