Instructions to use hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 432b7e6847b359a7e80e8658fcafbf6ad3d2ddc82dc99f6c9ac8cddb94e9a9f2
- Size of remote file:
- 544 kB
- SHA256:
- 7128a79a2102674ca298600d239221b4f215993eab95cefe60080f0495b5915f
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