Instructions to use mwesner/layoutlmv2-cord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mwesner/layoutlmv2-cord with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mwesner/layoutlmv2-cord")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("mwesner/layoutlmv2-cord") model = AutoModelForTokenClassification.from_pretrained("mwesner/layoutlmv2-cord", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53596265e1f3cdee40c28cc17040f07200cd261e17217d87b624e910520f718f
- Size of remote file:
- 802 MB
- SHA256:
- 82558e44b7981bf711cf54890d498c09941c08c6119aa0b5f4a51cb238aedf75
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