Instructions to use sofa566/layoutlmv2-base-uncased_finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sofa566/layoutlmv2-base-uncased_finetuned_docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="sofa566/layoutlmv2-base-uncased_finetuned_docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("sofa566/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("sofa566/layoutlmv2-base-uncased_finetuned_docvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6abcd64fad5a7db7013b006a561a5ffce21f107981a27a18fef4254b01684d6
- Size of remote file:
- 3.95 kB
- SHA256:
- 88c51ab2df663d2939cac3bdda366735b8ba3dd2d3c60afd487ea8d15baa378d
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