Instructions to use ghaith1997/layoutmv3-testing-document-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghaith1997/layoutmv3-testing-document-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ghaith1997/layoutmv3-testing-document-classification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("ghaith1997/layoutmv3-testing-document-classification") model = AutoModelForSequenceClassification.from_pretrained("ghaith1997/layoutmv3-testing-document-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25a47b32ee968873d92cddee82bddce8f431c3326fa37cff2ed5607b925cd7c2
- Size of remote file:
- 504 MB
- SHA256:
- b319def657676b77cf98db35b1d650ec7469195b780cf656aa2e52cd38074e22
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