Instructions to use hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("hugginglaoda/layoutlmv2-base-uncased_finetuned_docvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11e237ec6da2faccc836ef5b0f4d889f7d5a8d66bb0bb989ef7724e59a123b94
- Size of remote file:
- 802 MB
- SHA256:
- 8f20202b0040091ff42ed41f3269223f0660b6e4d6140473ae3b66accc735638
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