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:
- df0f5fc630770ae955e352ae6b97f06130b330e5c21e8f892d58a20b8e2ae892
- Size of remote file:
- 1.71 GB
- SHA256:
- 860bef3f8f93bfe5c330de3de7e48ff187c65b6e9e6fb4f36014d28aaac9bc63
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