| language: en | |
| license: cc-by-nc-sa-4.0 | |
| pipeline_tag: document-question-answering | |
| tags: | |
| - layoutlm | |
| - document-question-answering | |
| - invoices | |
| - endpoints-template | |
| 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" | |
| library_name: generic | |
| model-index: | |
| - name: layoutlm-invoices | |
| results: [] | |
| # 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 | |
|  | |
| ### After | |
| However this model is able to predict non-consecutive tokens and therefore the address correctly: | |
|  | |
| ## 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/). | |