Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
clip
feature-extraction
custom_code
Instructions to use chuhac/BiomedCLIP-vit-bert-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chuhac/BiomedCLIP-vit-bert-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="chuhac/BiomedCLIP-vit-bert-hf", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("chuhac/BiomedCLIP-vit-bert-hf", trust_remote_code=True) model = AutoModel.from_pretrained("chuhac/BiomedCLIP-vit-bert-hf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from chuhac/BiomedCLIP-vit-bert-hf: direct link, hf CLI and curl.
- Browser
- Download file 679 kB
-
https://huggingface.co/chuhac/BiomedCLIP-vit-bert-hf/resolve/main/tokenizer.json
- Command line
-
hf download hf://chuhac/BiomedCLIP-vit-bert-hf/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/chuhac/BiomedCLIP-vit-bert-hf/resolve/main/tokenizer.json
679 kB
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