Layoutlm Document Qa

🚀 LayoutLM for Visual Question Answering

A fine - tuned multi - modal LayoutLM model for document question - answering tasks, leveraging SQuAD2.0 and DocVQA datasets.

🚀 Quick Start

To run these examples, you must have PIL, pytesseract, and PyTorch installed in addition to transformers.

💻 Usage Examples

Basic Usage

from transformers import pipeline

nlp = pipeline(
    "document-question-answering",
    model="impira/layoutlm-document-qa",
)

nlp(
    "https://templates.invoicehome.com/invoice-template-us-neat-750px.png",
    "What is the invoice number?"
)
# {'score': 0.9943977, 'answer': 'us-001', 'start': 15, 'end': 15}

nlp(
    "https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg",
    "What is the purchase amount?"
)
# {'score': 0.9912159, 'answer': '$1,000,000,000', 'start': 97, 'end': 97}

nlp(
    "https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png",
    "What are the 2020 net sales?"
)
# {'score': 0.59147286, 'answer': '$ 3,750', 'start': 19, 'end': 20}

Advanced Usage

Since this model and pipeline was recently landed in transformers via PR #18407 and PR #18414, you'll need to use a recent version of transformers. For example:

pip install git+https://github.com/huggingface/transformers.git@2ef774211733f0acf8d3415f9284c49ef219e991

📚 Documentation

This is a fine - tuned version of the multi - modal LayoutLM model for the task of question answering on documents. It has been fine - tuned using both the SQuAD2.0 and DocVQA datasets.

📄 License

This project is under the MIT license.

👥 About Us

This model was created by the team at Impira.