Instructions to use Noureddinesa/Output_LayoutLMv3_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3_v2")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3_v2") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2567e3b511737d9a4965c8db555fbd0d4cd1ebf7293241c79920d9a84a84f530
- Size of remote file:
- 1.42 GB
- SHA256:
- 4c63b5cc6141d779f6141c8937f84559c4451f8dd3331b23ff354ae9dda6e19a
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