Instructions to use toolevalxm/MedDiagAI-ClinicalModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedDiagAI-ClinicalModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedDiagAI-ClinicalModel") 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("toolevalxm/MedDiagAI-ClinicalModel") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedDiagAI-ClinicalModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b5b05fa3f9a68f5079510006694eaac20f057e01626b86ca67eb39f63c3d951
- Size of remote file:
- 86 Bytes
- SHA256:
- c0ebe963326636856f556792486e63c18d8404d59647812c68bef099f97ed2c5
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