fimu-docproc-research/CIVQA-TesseractOCR
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How to use Sharka/CIVQA_LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("document-question-answering", model="Sharka/CIVQA_LayoutLMv3") # Load model directly
from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering
processor = AutoProcessor.from_pretrained("Sharka/CIVQA_LayoutLMv3")
model = AutoModelForDocumentQuestionAnswering.from_pretrained("Sharka/CIVQA_LayoutLMv3")This is a fine-tuned version of the LayoutLMv3 model, which was trained on Czech Invoice Visual Question Answering (CIVQA) datasets containing invoices in the Czech language.
This model enables Document Visual Question Answering on Czech invoices.
All invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.
You can find more information about this model in this paper.