Instructions to use Mahmoud7/LayoutLMv3_strat2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud7/LayoutLMv3_strat2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Mahmoud7/LayoutLMv3_strat2")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Mahmoud7/LayoutLMv3_strat2") model = AutoModelForTokenClassification.from_pretrained("Mahmoud7/LayoutLMv3_strat2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 640225cebbb1c7a108828005b2ed3437c0098fd1a7c44657550e7213d7bcac92
- Size of remote file:
- 504 MB
- SHA256:
- f2da4891a38b271616a11072b413d868700b7908868c8860c543a3ea46610a81
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