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 training_args.bin from oussama/layoutlmv3-finetuned-invoice: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/oussama/layoutlmv3-finetuned-invoice/resolve/main/training_args.bin
- Command line
-
hf download hf://oussama/layoutlmv3-finetuned-invoice/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/oussama/layoutlmv3-finetuned-invoice/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- c096b0eac8f41c47e86e43e7020badac7d9517c8500e8d1819c8cdb8fd5212bc
- Size of remote file:
- 4.09 kB
- SHA256:
- 1106b1ab1a117aad550b5c0011e45ccce7e9ba11b514c584dfe5b6ebcfb791a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.