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", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- b7735fff90f9d6b54d0bc421d8311f508d5ce8e66fde67ec6f3039e43eda7d8b
- Size of remote file:
- 5.69 kB
- SHA256:
- 7be9c5bdd64577403a9f0fdacf617b17aa38133b5add723145b6baf362b82f91
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