Instructions to use TomasFAV/Layoutlmv3InvoiceCzechV0123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomasFAV/Layoutlmv3InvoiceCzechV0123 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TomasFAV/Layoutlmv3InvoiceCzechV0123")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("TomasFAV/Layoutlmv3InvoiceCzechV0123") model = AutoModelForTokenClassification.from_pretrained("TomasFAV/Layoutlmv3InvoiceCzechV0123", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abdf9fedb5547f8e057b4aa0af3d5b3d871c45d154513b7100a9c53329d8af45
- Size of remote file:
- 5.27 kB
- SHA256:
- 58479f0e9cb4cb1716f8076e52799aabac4992718e06e2e0a6edae19498b879d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.