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
Create README.md
Browse files
README.md
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# The finetuned LayoutXLm model on Czech dataset for Visual Question Answering
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The original model can be found [here](microsoft/layoutxlm-base)
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The CIVQA dataset is the Czech Invoice dataset for Visual Question Answering
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Achieved results:
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eval_answer_text_recall = 0.7065
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eval_answer_text_f1 = 0.6998
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eval_answer_text_precision = 0.7319
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