Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use EMBO/sd-panelization-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBO/sd-panelization-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EMBO/sd-panelization-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EMBO/sd-panelization-v2") model = AutoModelForTokenClassification.from_pretrained("EMBO/sd-panelization-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8db4d7b2721d777e50a778e47f94d2c3b2e0cc5b02a9c7610a2491ff1fa6b00f
- Size of remote file:
- 1.33 GB
- SHA256:
- 32461e2a7352167c0e7e357825042ef0e491c4f39c8602d92fa8fa2da246ac1f
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