Instructions to use TusharGoel/LayoutLMv2-finetuned-docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TusharGoel/LayoutLMv2-finetuned-docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="TusharGoel/LayoutLMv2-finetuned-docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("TusharGoel/LayoutLMv2-finetuned-docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("TusharGoel/LayoutLMv2-finetuned-docvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4317f146b0dc262ad095196a293d3378dccff3d817a26f78fb6179a3a7a7427
- Size of remote file:
- 802 MB
- SHA256:
- 38686febf51a078e82fc6b3d8d75c60dc282ba1bdaa9a6c075416e218fc79b0b
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