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:
- dc90a85fadc5f797c98825ed582ab555237c4a77856084a411085e5b9f27172d
- Size of remote file:
- 20.1 kB
- SHA256:
- 8c3a389fcdc8c6ca7eca8446320e611dd976fa008552960d05f9db4c20757463
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