Instructions to use Sharka/CIVQA_DVQA_LayoutXLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharka/CIVQA_DVQA_LayoutXLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Sharka/CIVQA_DVQA_LayoutXLM")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("Sharka/CIVQA_DVQA_LayoutXLM") model = AutoModelForDocumentQuestionAnswering.from_pretrained("Sharka/CIVQA_DVQA_LayoutXLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 126f592004512bf3d29e7137f844ad63466c0dd7e1f8d8ea9baa6538445c73bf
- Size of remote file:
- 1.48 GB
- SHA256:
- 9a721a8452c7ec8ab1c1fdc8781e1cf95395dc773ffee372cff91c8d16345b9f
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