# pubmed-clip-vit-base-patch32 Zero-shot medical image classifier trained on biomedical image–text pairs.. **Details** - Model name: `pubmed-clip-vit-base-patch32` - Model source: [https://huggingface.co/flaviagiammarino/pubmed-clip-vit-base-patch32](https://huggingface.co/flaviagiammarino/pubmed-clip-vit-base-patch32) - Model author: Sedigheh Eslami et al. - Model license: MIT - Model size: 605.00 MB - Exposes embeddings? yes - Tags: `text-embeddings, classification, torch, official, medical, zero-shot, embeddings` **Requirements** - Packages: `torch, torchvision, transformers` - CPU support - yes - GPU support - yes **Example usage** ```python import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset( "coco-2017", split="validation", dataset_name=fo.get_default_dataset_name(), max_samples=50, shuffle=True, ) model = foz.load_zoo_model("pubmed-clip-vit-base-patch32") dataset.apply_model(model, label_field="predictions") session = fo.launch_app(dataset) ```