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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 479e665c350dbc25de4b06fd1f31d7ef74d4f8b5a426fda3c8d746093f074fcd
- Size of remote file:
- 528 kB
- SHA256:
- 987915c1e2d667ce5e392d6182095a31c56f39b086192bbd86bcf5b218795e8a
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