markuplm base finetuned websrc LLM Model

Introduction

MarkupLM is a multi-modal pre-training method designed to understand visually-rich documents by integrating text and markup language. It excels in tasks like webpage question answering and information extraction, achieving state-of-the-art results across various datasets. For comprehensive details, refer to the paper "MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding" by Junlong Li, Yiheng Xu, Lei Cui, and Furu Wei.

Architecture

MarkupLM leverages a multi-modal approach combining text and markup language to enhance document AI capabilities. This architecture is tailored for handling the complex structures and visual richness of documents like web pages.

Training

The model is pre-trained on datasets that include web pages, allowing it to understand and extract information from structurally complex documents effectively. The fine-tuning process targets specific tasks such as question answering and information extraction, optimizing the model for high performance.

Guide: Running Locally

To run MarkupLM locally:

Setup Environment: Ensure Python and PyTorch are installed. Set up a virtual environment for isolation.
Install Dependencies: Use pip to install required libraries like Hugging Face Transformers.
```
pip install transformers
```

Download the Model: Use the Transformers library to load the MarkupLM model.

from transformers import MarkupLMTokenizer, MarkupLMForQuestionAnswering

tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base-finetuned-websrc")
model = MarkupLMForQuestionAnswering.from_pretrained("microsoft/markuplm-base-finetuned-websrc")

Run Inference: Prepare your input data and use the model to perform tasks like question answering.
Utilize Cloud GPUs: For efficiency, especially with large datasets, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The use of MarkupLM is subject to the terms specified by Microsoft under the respective model or dataset licenses. Ensure compliance with these terms when using or distributing the model.