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:
- f334c3512b20e77b879c844aa266cced0f394ceb2c116f2b4e0e51668c2327ad
- Size of remote file:
- 29.8 kB
- SHA256:
- 3b7344381ef23c4ab7bfe14e243995357c9c546d21cdea214f855fcba092bac7
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