Instructions to use ghaith1997/layoutlmv3-documents-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghaith1997/layoutlmv3-documents-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ghaith1997/layoutlmv3-documents-classification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("ghaith1997/layoutlmv3-documents-classification") model = AutoModelForSequenceClassification.from_pretrained("ghaith1997/layoutlmv3-documents-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40b8a8f73d8e4bf068fafdf616c737acc8727aa09b0476114a1f98850d251b2a
- Size of remote file:
- 504 MB
- SHA256:
- fda89c30f141be2b6699ae07a4c2bbaa774d16bfad133f93286e6d06d97559b8
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