Instructions to use hf-tiny-model-private/tiny-random-LayoutLMv3ForSequenceClassification 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-LayoutLMv3ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-LayoutLMv3ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4273b71b51acd56aeaad0e4b3f9277f8a609d567eb01209cf688142a0a605303
- Size of remote file:
- 444 kB
- SHA256:
- 3436df05c23f1f4d34b9f60c909122a42ffb1062541a988545b63a99590c9fe0
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