Instructions to use tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f7dab2cbf7a7ec0aec85c09d4c330bf6f3b53f984487ba879e3505584751db4
- Size of remote file:
- 1.71 GB
- SHA256:
- a2d3f642e4a13daa4388b8a5bcbdc59122d7ed5e9de7b8d1af3ee2281698a193
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