Instructions to use hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification 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-LayoutLMv3ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70b6a243c71496d09c8d0793aa3c171f4ec11697f5986409b9e4700f77e0dfd9
- Size of remote file:
- 439 kB
- SHA256:
- cd53d7429e87684eb2e0383e2d88eb8e21445c666b975317e103f2ac1c85adc7
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