Instructions to use Noureddinesa/Output_LayoutLMv3_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3_v3")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3_v3") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77ff497e418d8cee2810920ee058230d0a66878c6a4a891c34f7ea25e435e0ad
- Size of remote file:
- 4.92 kB
- SHA256:
- c880bff34a3d7db4ee728e224e839a12e50a864ab22a19da9eca019add93bb40
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