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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c471493b4c6674cd241550f1a03322582fc553117360b3bca38a5bc500ca48d5
- Size of remote file:
- 438 kB
- SHA256:
- da97349841058a7aff7d0a120defcffdea42371662146315dc3a3eef0974ac30
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