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:
- 91df357199661b57d136c22c4def68e503da12dbf3ed2a8ca7189bddeb98fdbf
- Size of remote file:
- 2.86 kB
- SHA256:
- b3eea485964cdf2e2a59f98a39e5b2bbbf2f0bd628ce48114c6c6dde1ee0614e
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