Instructions to use jiy03150/layoutlmv2-base-uncased_finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jiy03150/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="jiy03150/layoutlmv2-base-uncased_finetuned_docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("jiy03150/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("jiy03150/layoutlmv2-base-uncased_finetuned_docvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2320c7ba95be80476f0f952b56555d378a520cf5a98d96b62d8786c525e869f
- Size of remote file:
- 4.09 kB
- SHA256:
- dc7b4e52009abed92ee3436dafa51cdc0c0cf913dc243a64e6d9b8a4cc671d8c
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