Instructions to use navodPeiris/layoutlmv2-document-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use navodPeiris/layoutlmv2-document-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="navodPeiris/layoutlmv2-document-classifier")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("navodPeiris/layoutlmv2-document-classifier") model = AutoModelForSequenceClassification.from_pretrained("navodPeiris/layoutlmv2-document-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a50935c4f3dc5ff61a1c97bf62f29f0f4ffdd7b8d941924d4ac173871438fc5e
- Size of remote file:
- 5.37 kB
- SHA256:
- 1cd63c02b93f2ac80b5e57f7422f44b93b004f810e2f190e473e7d489d2a2aeb
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