Instructions to use rpii2023/finale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rpii2023/finale with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rpii2023/finale")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("rpii2023/finale") model = AutoModelForTokenClassification.from_pretrained("rpii2023/finale", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47b014d14fc382fcafdd4c6fac2d2586e731196503929988ebb2c4e9b4b1cb2a
- Size of remote file:
- 2.86 GB
- SHA256:
- ff6fb10e1759af67a90e1a6900d826579936731d9448b465de4e65f7509041d4
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