Instructions to use Noureddinesa/Output_LayoutLMv3_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noureddinesa/Output_LayoutLMv3_v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Noureddinesa/Output_LayoutLMv3_v5")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Noureddinesa/Output_LayoutLMv3_v5") model = AutoModelForTokenClassification.from_pretrained("Noureddinesa/Output_LayoutLMv3_v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 903a98ad179d3412c34af28e7c2908984f00e252ecd33374a9c1741deb93fe83
- Size of remote file:
- 1.42 GB
- SHA256:
- b016ede4599b60584df6b76be84f5bd7409b21f9c26eb0493550e10bac197e81
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