readme: ---
  language: en
  license: cc-by-nc-sa-4.0
  pipeline_tag: document-question-answering
  tags:
   - layoutlm
   - document-question-answering
   - pdf
   - invoices
  widget:
  - text: "What is the invoice number?"
    src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
  - text: "What is the purchase amount?"
    src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
  ---
  
  # LayoutLM for Invoices
  
  This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of
  invoices as well as both [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) for general comprehension.
  
  ## Non-consecutive tokens
  
  Unlike other QA models, which can only extract consecutive tokens (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional
  classifier head. For example, QA models often encounter this failure mode:
  
  ### Before
  
  ![Broken Address](./before.png)
  
  
  ### After
  
  However this model is able to predict non-consecutive tokens and therefore the address correctly:
  
  ![Two-line Address](./after.png)
  
  ## Getting started with the model
  
  The best way to use this model is via [DocQuery](https://github.com/impira/docquery).
  
  ## About us
  
  This model was created by the team at [Impira](https://www.impira.com/).
  