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:
- ca1c11197236c8cf9f3793b9b9226afb7a636a7badb3c8a088cde2c1b1fa1282
- Size of remote file:
- 496 MB
- SHA256:
- c92be69ab7ebeebf6ad7a61296b3c0ec65c40e5490ab299ae1b3160e8d6ecf1f
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