Instructions to use hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering") model = AutoModelForDocumentQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6211e952d30dada83b7337d799b2f16e35bad46c8931248031a16d7419238d4
- Size of remote file:
- 535 kB
- SHA256:
- 70450d3a809079457455dcd0e7328adc0520781f25cc45d2d08a39c5d6f6c3e4
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