Instructions to use fimu-docproc-research/CIVQA_layoutXLM_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fimu-docproc-research/CIVQA_layoutXLM_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="fimu-docproc-research/CIVQA_layoutXLM_model")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("fimu-docproc-research/CIVQA_layoutXLM_model") model = AutoModelForDocumentQuestionAnswering.from_pretrained("fimu-docproc-research/CIVQA_layoutXLM_model") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
The finetuned LayoutXLm model on Czech dataset for Visual Question Answering
The original model can be found here
The CIVQA dataset is the Czech Invoice dataset for Visual Question Answering
Achieved results:
eval_answer_text_recall = 0.7065
eval_answer_text_f1 = 0.6998
eval_answer_text_precision = 0.7319
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