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:
- 5c71d33c60a1d93ddb9bbbd8fc50a5bd72562abd3d4bb3645e71625a9cb99e0b
- Size of remote file:
- 444 kB
- SHA256:
- 990d50ebae88d4fab72b2c8a14631f9904d41ac92e22a1b52f84e55626c97e61
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