Instructions to use Sharka/CIVQA_LayoutXLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharka/CIVQA_LayoutXLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Sharka/CIVQA_LayoutXLM")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("Sharka/CIVQA_LayoutXLM") model = AutoModelForDocumentQuestionAnswering.from_pretrained("Sharka/CIVQA_LayoutXLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd2ac4d6c6b69fb104990bf820cd2a37cac6cd17b759b3599877f8276ba170c6
- Size of remote file:
- 5.77 MB
- SHA256:
- 3141add60977a1af742478587abdcb8917865b3c722f1535c20483a47754aaf4
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