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:
- bd36edde55aacf0f78ae5cdacf957132fe0f086ae735753b989cfa3e65ef1001
- Size of remote file:
- 2.99 kB
- SHA256:
- 0deeda0a4b223a2fc981dd5a3cab7e691fbba9d0a5bd8d523cee0903f9a0aa51
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.