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:
- f094bff08826f18f910edb0f089f00a16cc6ce1c409bfedbed9c7022b146550d
- Size of remote file:
- 343 MB
- SHA256:
- 5c59148ddc7143f20eecd0572bb0743c2a3aaf102e16c79d927161f4cfb7731b
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