Instructions to use Sharka/CIVQA_LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharka/CIVQA_LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Sharka/CIVQA_LayoutLMv3")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("Sharka/CIVQA_LayoutLMv3") model = AutoModelForDocumentQuestionAnswering.from_pretrained("Sharka/CIVQA_LayoutLMv3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e41051171fa22c54bda098fcd8d16810af1b3ec7ce841935bc4467a4e73aca5f
- Size of remote file:
- 5.77 MB
- SHA256:
- 7ca558db16a3060ef9c9f365a88534143fbfec22c0eee50a2ac9d589a4a143f9
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