Instructions to use cringgaard/extrapolation_length with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cringgaard/extrapolation_length with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cringgaard/extrapolation_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/extrapolation_length") model = AutoModelForZeroShotImageClassification.from_pretrained("cringgaard/extrapolation_length") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cdb326b34d409244bfd7ecb6ac4cbd158fb13c07fb3ccaac24fc9a74b277945
- Size of remote file:
- 1.71 GB
- SHA256:
- 6764f0d33c9489179f4bc48d6ef233bedab0dc4cdcb912b54138745d0b22701c
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