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language: en
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license: mit
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---
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language: en
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license: mit
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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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---
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# LayoutLM for Visual Question Answering
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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 documents. It has been fine-tuned using both the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) datasets.
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## Getting started with the model
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To run these examples, you must have [PIL](https://pillow.readthedocs.io/en/stable/installation.html), [pytesseract](https://pypi.org/project/pytesseract/), and [PyTorch](https://pytorch.org/get-started/locally/) installed in addition to [transformers](https://huggingface.co/docs/transformers/index).
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```python
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from transformers import pipeline
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nlp = pipeline(
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"document-question-answering",
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model="impira/layoutlm-document-qa",
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)
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nlp(
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"https://templates.invoicehome.com/invoice-template-us-neat-750px.png",
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"What is the invoice number?"
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)
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# {'score': 0.9943977, 'answer': 'us-001', 'start': 15, 'end': 15}
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nlp(
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"https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg",
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"What is the purchase amount?"
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)
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# {'score': 0.9912159, 'answer': '$1,000,000,000', 'start': 97, 'end': 97}
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nlp(
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"https://www.accountingcoach.com/wp-content/uploads/2013/10/[email protected]",
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"What are the 2020 net sales?"
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)
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# {'score': 0.59147286, 'answer': '$ 3,750', 'start': 19, 'end': 20}
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```
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**NOTE**: This model and pipeline was recently landed in transformers via [PR #18407](https://github.com/huggingface/transformers/pull/18407) and [PR #18414](https://github.com/huggingface/transformers/pull/18414), so you'll need to use a recent version of transformers, for example:
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```bash
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pip install git+https://github.com/huggingface/transformers.git@2ef774211733f0acf8d3415f9284c49ef219e991
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```
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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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