Instructions to use Sharka/CIVQA_DVQA_LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharka/CIVQA_DVQA_LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Sharka/CIVQA_DVQA_LayoutLMv3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("Sharka/CIVQA_DVQA_LayoutLMv3") model = AutoModelForDocumentQuestionAnswering.from_pretrained("Sharka/CIVQA_DVQA_LayoutLMv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature# LayoutLMv3 Model Fine-tuned with CIVQA (Tesseract) dataset This is a fine-tuned version of the [LayoutLMv3 model](https://huggingface.co/microsoft/layoutlmv3-base), which was trained on Czech Invoice Visual Question Answering (CIVQA) dataset containing invoices in the Czech language as well as on the Data Visualizations via Question Answering ([DVQA] (https://paperswithcode.com/dataset/dvqa)) dataset. This model enables Document Visual Question Answering on Czech invoices with the use of the existing DVQA dataset. Regarding the Czech invoices, we focused on 10 different entities, which are crucial for processing the invoices. - Variable symbol - Specific symbol - Constant symbol - Bank code - Account number - Total amount - Invoice date - Name of supplier - DIC - QR codee93fc55
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