impira/layoutlm-document-qa

LayoutLM for Visual Question Answering

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.

Getting started with the model

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

```python 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/[email protected]", "What are the 2020 net sales?" ) # {'score': 0.59147286, 'answer': '$ 3,750', 'start': 19, 'end': 20} ```

NOTE: This model and pipeline was recently landed in transformers via PR #18407 and PR #18414, so you'll need to use a recent version of transformers, for example:

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

About us

This model was created by the team at Impira.