YuukiAsuna/VietnameseTableVQA
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How to use YuukiAsuna/VieTable-donut-docvqa-demo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("document-question-answering", model="YuukiAsuna/VieTable-donut-docvqa-demo") # Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText
tokenizer = AutoTokenizer.from_pretrained("YuukiAsuna/VieTable-donut-docvqa-demo")
model = AutoModelForImageTextToText.from_pretrained("YuukiAsuna/VieTable-donut-docvqa-demo")VieTable Donut DocVQA is a fine-tuned version of the Donut model for the Vietnamese DocVQA (Table data)
@article{DBLP:journals/corr/abs-2111-15664,
author = {Geewook Kim and
Teakgyu Hong and
Moonbin Yim and
Jinyoung Park and
Jinyeong Yim and
Wonseok Hwang and
Sangdoo Yun and
Dongyoon Han and
Seunghyun Park},
title = {Donut: Document Understanding Transformer without {OCR}},
journal = {CoRR},
volume = {abs/2111.15664},
year = {2021},
url = {https://arxiv.org/abs/2111.15664},
eprinttype = {arXiv},
eprint = {2111.15664},
timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Base model
naver-clova-ix/donut-base