
# MarkupLM, fine-tuned on WebSRC

**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**

## Introduction

MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:

[MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518)  Junlong Li, Yiheng Xu, Lei Cui, Furu Wei

## Usage

We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/markuplm) and [demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM).
{"language": ["en"], "datasets": ["websrc"], "inference": false}
transformers pytorch markuplm question-answering en dataset:websrc arxiv:2110.08518 has_space region:us
TAGS
#transformers #pytorch #markuplm #question-answering #en #dataset-websrc #arxiv-2110.08518 #has_space #region-us 


# MarkupLM, fine-tuned on WebSRC

Multimodal (text +markup language) pre-training for Document AI

## Introduction

MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:

MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding  Junlong Li, Yiheng Xu, Lei Cui, Furu Wei

## Usage

We refer to the docs and demo notebooks.
# MarkupLM, fine-tuned on WebSRC

Multimodal (text +markup language) pre-training for Document AI ## Introduction

MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:

MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding  Junlong Li, Yiheng Xu, Lei Cui, Furu Wei ## Usage

We refer to the docs and demo notebooks.
TAGS
#transformers #pytorch #markuplm #question-answering #en #dataset-websrc #arxiv-2110.08518 #has_space #region-us 
 # MarkupLM, fine-tuned on WebSRC

Multimodal (text +markup language) pre-training for Document AI ## Introduction

MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:

MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding  Junlong Li, Yiheng Xu, Lei Cui, Furu Wei ## Usage

We refer to the docs and demo notebooks.
passage: TAGS
#transformers #pytorch #markuplm #question-answering #en #dataset-websrc #arxiv-2110.08518 #has_space #region-us 
# MarkupLM, fine-tuned on WebSRC

Multimodal (text +markup language) pre-training for Document AI## Introduction

MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:

MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding  Junlong Li, Yiheng Xu, Lei Cui, Furu Wei## Usage

We refer to the docs and demo notebooks.