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
layoutlmv2-base-uncased-finetuned-infovqa / runs /Nov01_14-49-18_6b7ab81d9799 /1635778178.4108288 /events.out.tfevents.1635778178.6b7ab81d9799.78.1
- Xet hash:
- 3fac0ef7135ef13c73407fc30451ebf0bca37bd8a0d047823885c093a54aa42a
- Size of remote file:
- 4.59 kB
- SHA256:
- 7006186e826098667f83d156b5190420c059c6297ff53cbdf10de16e870e0918
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