Instructions to use hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e1d2d586fe376a27b3926ff6fcb6d6ee0f378cde28755053e6c875db055b1b4
- Size of remote file:
- 539 kB
- SHA256:
- 8244f1da2d5f17dce8250e2419f6bfd8acc103d2061559589a450b68a045abf6
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