Instructions to use J-RUM/professions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use J-RUM/professions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="J-RUM/professions") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("J-RUM/professions") model = AutoModelForImageClassification.from_pretrained("J-RUM/professions") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- c3c56b4ccfc7e3c4f3e003207c8f739634cf0835c045f3be7c96cf8d7e05bb84
- Size of remote file:
- 27.1 kB
- SHA256:
- e4aeebff20d7799a56dfb621a4351ca19d57bb192a06a8c34693ad1d4958769b
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