Instructions to use cringgaard/interpolation_year_built with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cringgaard/interpolation_year_built with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cringgaard/interpolation_year_built") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("cringgaard/interpolation_year_built") model = AutoModelForZeroShotImageClassification.from_pretrained("cringgaard/interpolation_year_built", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6915c6f18128a8cb59a3cec853348b085d60cbdb3d6e83953c37e4c64b9794a1
- Size of remote file:
- 5.37 kB
- SHA256:
- 72b6f28bc3e8aa6ee8abcd16990ca70809b9edb50d7c6e24b5597a936ee079b2
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