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:
- fe1120ed82aec2d7c88529a10db139bf4575e3fe03741e92022ea8fc321c835a
- Size of remote file:
- 343 MB
- SHA256:
- c0e640901dcc008ace0feef4c6032bb0442d75837c39b95976ac3ca41ed6285e
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