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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38dcfff41137d81b8fa0c85821a8d7ff049ff73137f45f448be7760d9ae051cf
- Size of remote file:
- 4.92 kB
- SHA256:
- 5525387e5d3f11017cc3c10370249d20b383f9e950fa555fb4adbdfd86f132eb
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