Instructions to use am-infoweb/layoutlmv2-finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use am-infoweb/layoutlmv2-finetuned_docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="am-infoweb/layoutlmv2-finetuned_docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("am-infoweb/layoutlmv2-finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("am-infoweb/layoutlmv2-finetuned_docvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97b3635b88600e5df0f57089b07eb40f09f2d9823eed5c8835ede89d50fe3518
- Size of remote file:
- 4.09 kB
- SHA256:
- 352b8bd7a55dd874a7e2fc203bc027e605f74edd51e28204b3ee7eefee969f3e
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