Instructions to use humane-lab/CFT-CLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use humane-lab/CFT-CLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="humane-lab/CFT-CLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("humane-lab/CFT-CLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("humane-lab/CFT-CLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Model Details
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The CFT-CLIP was developed by HUMANE Lab researchers at Soongsil University to assess news thumbnail representativeness by counterfactual text-guided contrastive language-image pretraining.
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language:
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- en
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## Model Details
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The CFT-CLIP was developed by HUMANE Lab researchers at Soongsil University to assess news thumbnail representativeness by counterfactual text-guided contrastive language-image pretraining.
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