
# 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) 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.
- Invoice number
- Variable symbol
- Specific symbol
- Constant symbol
- Bank code
- Account number
- ICO
- Total amount
- Invoice date
- Due date
- Name of supplier
- IBAN
- DIC
- QR code
- Supplier's address

You can find more information about this model in this [paper](https://nlp.fi.muni.cz/raslan/raslan23.pdf#page=31).
{"language": ["cs"], "license": "mit", "tags": ["Document Question Answering"], "datasets": ["fimu-docproc-research/CIVQA-TesseractOCR"]}
transformers pytorch layoutlmv3 document-question-answering Document Question Answering cs dataset:fimu-docproc-research/CIVQA-TesseractOCR license:mit endpoints_compatible region:us