Instructions to use sDenisov3/layoutlmv2-base-uncased_finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sDenisov3/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="sDenisov3/layoutlmv2-base-uncased_finetuned_docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("sDenisov3/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("sDenisov3/layoutlmv2-base-uncased_finetuned_docvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1679b2025d889fbfec6b670d5928039e173c8a378f15b62678f34f10bbf73acd
- Size of remote file:
- 802 MB
- SHA256:
- 17b253b7259430d53120f13a698e9be4900f5c94456f5c81298e0c2936f6a2c3
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