Instructions to use Cem13/med_complication_classifaction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cem13/med_complication_classifaction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Cem13/med_complication_classifaction") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Cem13/med_complication_classifaction") model = AutoModelForImageClassification.from_pretrained("Cem13/med_complication_classifaction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Cem13/med_complication_classifaction: direct link, hf CLI and curl.
- Browser
- Download file 4.02 kB
-
https://huggingface.co/Cem13/med_complication_classifaction/resolve/main/training_args.bin
- Command line
-
hf download hf://Cem13/med_complication_classifaction/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Cem13/med_complication_classifaction/resolve/main/training_args.bin
4.02 kB
- Xet hash:
- f6f4b2e7422c3969826e1f6a6bdc7a2111f01197e87c2fe4739cb587574776f4
- Size of remote file:
- 4.02 kB
- SHA256:
- 4de3cea24e6f72d85ca56d0b1d934558031b5c89eec018305c9bedfec4dfb212
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