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--- |
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language: en |
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license: cc-by-nc-sa-4.0 |
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pipeline_tag: document-question-answering |
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tags: |
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- layoutlm |
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- document-question-answering |
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- pdf |
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- invoices |
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widget: |
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- text: "What is the invoice number?" |
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src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" |
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- text: "What is the purchase amount?" |
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src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg" |
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--- |
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# LayoutLM for Invoices |
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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 |
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invoices as well as both [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) for general comprehension. |
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## Non-consecutive tokens |
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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 |
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classifier head. For example, QA models often encounter this failure mode: |
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### Before |
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### After |
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However this model is able to predict non-consecutive tokens and therefore the address correctly: |
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## Getting started with the model |
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The best way to use this model is via [DocQuery](https://github.com/impira/docquery). |
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## About us |
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This model was created by the team at [Impira](https://www.impira.com/). |
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