Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
layoutlmv3
Generated from Trainer
Eval Results (legacy)
Instructions to use oussama/layoutlmv3-finetuned-invoice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oussama/layoutlmv3-finetuned-invoice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="oussama/layoutlmv3-finetuned-invoice")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("oussama/layoutlmv3-finetuned-invoice") model = AutoModelForTokenClassification.from_pretrained("oussama/layoutlmv3-finetuned-invoice") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03eea27dff6c891e0dc364d3d1a6de7a941fa65360d6a20c998a28799fa6dc45
- Size of remote file:
- 504 MB
- SHA256:
- 5d07782f3f5aa5a7dca90e92f435c4c76c9070dc8bc477156c12d5f1bca288d4
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