Instructions to use EMBO/sd-panelization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBO/sd-panelization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EMBO/sd-panelization")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EMBO/sd-panelization") model = AutoModelForTokenClassification.from_pretrained("EMBO/sd-panelization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ab196267c91d12f1b55a7c0253bbd982fcd52b2a2b0a9750709782870ed45f2
- Size of remote file:
- 496 MB
- SHA256:
- 70b610bdfe678e74736c398d117642f66bba18e857a45b3551141ac16b39bc23
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