

 
 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)