Instructions to use Noureddinesa/Output_LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 949a08e5c3f35b294bfd70c0d338be106e0052420e28566fef8da85c90360325
- Size of remote file:
- 1.42 GB
- SHA256:
- d289d45e7c2764b4259fcd11f0a8372278d251d4f6ffef307e20784181f198c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.