Instructions to use hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hugginglaoda/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="hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b96d97a0b77ece7a886b7f6963a05152efd1a406fce37f23d7b15f98975f3e81
- Size of remote file:
- 3.58 kB
- SHA256:
- 7bc71d9de642ba92584466d78bf47766da08a3af05faa98533436b97183f81f6
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