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:
- 95404e6c8bcb3db953db1ecc182cc22578e1a022d1e8b35407362744026dad83
- Size of remote file:
- 802 MB
- SHA256:
- e682146bdef1824263dda3ec407c61e2b3a85fbce62a23d06642f9dfedf42eb1
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