Instructions to use tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8239ddc13d6d324eb251922076fe50df7a13db2da1c933a0d5b3faee6b38730f
- Size of remote file:
- 802 MB
- SHA256:
- 08ab52a472e774a870d49d57205fadf87b8cc6b8fa57893d614d84337a6b7062
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