Instructions to use hf-tiny-model-private/tiny-random-LayoutLMv3Model 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-LayoutLMv3Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-LayoutLMv3Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49cc2f637d796a9d71aa61fb47a7783e9682dc495a2cf553ba0923bf14b62c6c
- Size of remote file:
- 451 kB
- SHA256:
- e99073fee4c0c630db619404f57635498313858e1f70e53ab220185d49417a6c
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