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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 365 Bytes
5da3e0d bf136d4 5da3e0d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # The finetuned LayoutXLm model on Czech dataset for Visual Question Answering
The original model can be found [here](microsoft/layoutxlm-base)
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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