metadata
language:
- pt
- en
license: apache-2.0
pipeline_tag: document-question-answering
tags:
- document-question-answering
- pdf
widget:
- text: Qual é o número da fatura?
src: >-
https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png
- text: Qual é o valor da compra?
src: >-
https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg
library_name: adapter-transformers
Getting started with the model
To run these examples, you must have PIL, pytesseract, and PyTorch installed in addition to transformers.
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:
pip install git+https://github.com/huggingface/transformers.git@2ef774211733f0acf8d3415f9284c49ef219e991