# MarkupLM

## Overview

The MarkupLM model was proposed in [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document

Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei. MarkupLM is BERT, but

applied to HTML pages instead of raw text documents. The model incorporates additional embedding layers to improve

performance, similar to [LayoutLM](layoutlm).

The model can be used for tasks like question answering on web pages or information extraction from web pages. It obtains

state-of-the-art results on 2 important benchmarks:

- [WebSRC](https://x-lance.github.io/WebSRC/), a dataset for Web-Based Structural Reading Comprehension (a bit like SQuAD but for web pages)

- [SWDE](https://www.researchgate.net/publication/221299838_From_one_tree_to_a_forest_a_unified_solution_for_structured_web_data_extraction), a dataset

for information extraction from web pages (basically named-entity recogntion on web pages)

The abstract from the paper is the following:

*Multimodal pre-training with text, layout, and image has made significant progress for Visually-rich Document

Understanding (VrDU), especially the fixed-layout documents such as scanned document images. While, there are still a

large number of digital documents where the layout information is not fixed and needs to be interactively and

dynamically rendered for visualization, making existing layout-based pre-training approaches not easy to apply. In this

paper, we propose MarkupLM for document understanding tasks with markup languages as the backbone such as

HTML/XML-based documents, where text and markup information is jointly pre-trained. Experiment results show that the

pre-trained MarkupLM significantly outperforms the existing strong baseline models on several document understanding

tasks. The pre-trained model and code will be publicly available.*

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

## Usage tips

- In addition to `input_ids`, [`~MarkupLMModel.forward`] expects 2 additional inputs, namely `xpath_tags_seq` and `xpath_subs_seq`.

These are the XPATH tags and subscripts respectively for each token in the input sequence.

- One can use [`MarkupLMProcessor`] to prepare all data for the model. Refer to the [usage guide](#usage-markuplmprocessor) for more info.

 MarkupLM architecture. Taken from the original paper. 

## Usage: MarkupLMProcessor

The easiest way to prepare data for the model is to use [`MarkupLMProcessor`], which internally combines a feature extractor

([`MarkupLMFeatureExtractor`]) and a tokenizer ([`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]). The feature extractor is

used to extract all nodes and xpaths from the HTML strings, which are then provided to the tokenizer, which turns them into the

token-level inputs of the model (`input_ids` etc.). Note that you can still use the feature extractor and tokenizer separately,

if you only want to handle one of the two tasks.

thon

from transformers import MarkupLMFeatureExtractor, MarkupLMTokenizerFast, MarkupLMProcessor

feature_extractor = MarkupLMFeatureExtractor()

tokenizer = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base")

processor = MarkupLMProcessor(feature_extractor, tokenizer)

In short, one can provide HTML strings (and possibly additional data) to [`MarkupLMProcessor`],

and it will create the inputs expected by the model. Internally, the processor first uses

[`MarkupLMFeatureExtractor`] to get a list of nodes and corresponding xpaths. The nodes and

xpaths are then provided to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`], which converts them

to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_subs_seq`, `xpath_tags_seq`.

Optionally, one can provide node labels to the processor, which are turned into token-level `labels`.

[`MarkupLMFeatureExtractor`] uses [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/), a Python library for

pulling data out of HTML and XML files, under the hood. Note that you can still use your own parsing solution of

choice, and provide the nodes and xpaths yourself to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`].

In total, there are 5 use cases that are supported by the processor. Below, we list them all. Note that each of these

use cases work for both batched and non-batched inputs (we illustrate them for non-batched inputs).

**Use case 1: web page classification (training, inference) + token classification (inference), parse_html = True**

This is the simplest case, in which the processor will use the feature extractor to get all nodes and xpaths from the HTML.

thon

>>> from transformers import MarkupLMProcessor

>>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")

>>> html_string = """

  html

  

  

  Hello world

  

  

  Welcome

  Here is my website.

  

  """

>>> # note that you can also add provide all tokenizer parameters here such as padding, truncation

>>> encoding = processor(html_string, return_tensors="pt")

>>> print(encoding.keys())

dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

**Use case 2: web page classification (training, inference) + token classification (inference), parse_html=False**

In case one already has obtained all nodes and xpaths, one doesn't need the feature extractor. In that case, one should

provide the nodes and corresponding xpaths themselves to the processor, and make sure to set `parse_html` to `False`.

thon

>>> from transformers import MarkupLMProcessor

>>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")

>>> processor.parse_html = False

>>> nodes = ["hello", "world", "how", "are"]

>>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]

>>> encoding = processor(nodes=nodes, xpaths=xpaths, return_tensors="pt")

>>> print(encoding.keys())

dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

**Use case 3: token classification (training), parse_html=False**

For token classification tasks (such as [SWDE](https://paperswithcode.com/dataset/swde)), one can also provide the

corresponding node labels in order to train a model. The processor will then convert these into token-level `labels`.

By default, it will only label the first wordpiece of a word, and label the remaining wordpieces with -100, which is the

`ignore_index` of PyTorch's CrossEntropyLoss. In case you want all wordpieces of a word to be labeled, you can

initialize the tokenizer with `only_label_first_subword` set to `False`.

thon

>>> from transformers import MarkupLMProcessor

>>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")

>>> processor.parse_html = False

>>> nodes = ["hello", "world", "how", "are"]

>>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]

>>> node_labels = [1, 2, 2, 1]

>>> encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")

>>> print(encoding.keys())

dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq', 'labels'])

**Use case 4: web page question answering (inference), parse_html=True**

For question answering tasks on web pages, you can provide a question to the processor. By default, the

processor will use the feature extractor to get all nodes and xpaths, and create [CLS] question tokens [SEP] word tokens [SEP].

thon

>>> from transformers import MarkupLMProcessor

>>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")

>>> html_string = """

  html

  

  

  Hello world

  

  

  Welcome

  My name is Niels.

  

  """

>>> question = "What's his name?"

>>> encoding = processor(html_string, questions=question, return_tensors="pt")

>>> print(encoding.keys())

dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

**Use case 5: web page question answering (inference), parse_html=False**

For question answering tasks (such as WebSRC), you can provide a question to the processor. If you have extracted

all nodes and xpaths yourself, you can provide them directly to the processor. Make sure to set `parse_html` to `False`.

thon

>>> from transformers import MarkupLMProcessor

>>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")

>>> processor.parse_html = False

>>> nodes = ["hello", "world", "how", "are"]

>>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]

>>> question = "What's his name?"

>>> encoding = processor(nodes=nodes, xpaths=xpaths, questions=question, return_tensors="pt")

>>> print(encoding.keys())

dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

## Resources

- [Demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM)

- [Text classification task guide](../tasks/sequence_classification)

- [Token classification task guide](../tasks/token_classification)

- [Question answering task guide](../tasks/question_answering)

## MarkupLMConfig

[[autodoc]] MarkupLMConfig

    - all

## MarkupLMFeatureExtractor

[[autodoc]] MarkupLMFeatureExtractor

    - __call__

## MarkupLMTokenizer

[[autodoc]] MarkupLMTokenizer

    - build_inputs_with_special_tokens

    - get_special_tokens_mask

    - create_token_type_ids_from_sequences

    - save_vocabulary

## MarkupLMTokenizerFast

[[autodoc]] MarkupLMTokenizerFast

    - all

## MarkupLMProcessor

[[autodoc]] MarkupLMProcessor

    - __call__

## MarkupLMModel

[[autodoc]] MarkupLMModel

    - forward

## MarkupLMForSequenceClassification

[[autodoc]] MarkupLMForSequenceClassification

    - forward

## MarkupLMForTokenClassification

[[autodoc]] MarkupLMForTokenClassification

    - forward

## MarkupLMForQuestionAnswering

[[autodoc]] MarkupLMForQuestionAnswering

    - forward