Instructions to use cringgaard/interpolation_length with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cringgaard/interpolation_length with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cringgaard/interpolation_length") 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_length") model = AutoModelForZeroShotImageClassification.from_pretrained("cringgaard/interpolation_length") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9912efc965cf833b588f63be141814f28221a514d295436b876b4f12b2cd8597
- Size of remote file:
- 1.71 GB
- SHA256:
- 9a309faa6d8cd81100f913af31f2f93216560082492d0eb8b6c3babb4c394f9c
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