Instructions to use Professor/CGIAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Professor/CGIAR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Professor/CGIAR") 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("Professor/CGIAR") model = AutoModelForImageClassification.from_pretrained("Professor/CGIAR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 640dc7c883f500af772e284c8f2aba7e1b7baa525ae9d65ff0606aa862b99c60
- Size of remote file:
- 4.28 kB
- SHA256:
- 1a2ac96f2f9021869e90de8c212be58b77cc2cc6d46bda7635e1116934ea6b8d
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