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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from oussama/layoutlmv3-finetuned-invoice: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/oussama/layoutlmv3-finetuned-invoice/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://oussama/layoutlmv3-finetuned-invoice/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/oussama/layoutlmv3-finetuned-invoice/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- 3b5effc6fd43159519c5142422b8f182962f3cf1365e2ca389da7ccd60607668
- Size of remote file:
- 504 MB
- SHA256:
- 3e1b0ea011a8a9e06d812599f2de507b3184456f87c7ed798ec8d6dbce0433b3
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