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:
- 733ef7a67cf114064572dda5e3016f44288ff134c61f396539edf741fcca6444
- Size of remote file:
- 43 kB
- SHA256:
- 6abafa0d0809aef58a2315c8b9c994425f71f2e8fc2951383ab288a21efbd012
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