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:
- 66a1e0dc889d3b4df48321f7823d5193cb0eae4d1cd8847fa7a514c6b4e137b1
- Size of remote file:
- 3.06 kB
- SHA256:
- 2280d1754fe09f3f546bd156a40d47620bd4f0446435eb2e8b5c3429ee0b2406
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