Instructions to use cringgaard/extrapolation_year_built with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cringgaard/extrapolation_year_built with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cringgaard/extrapolation_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/extrapolation_year_built") model = AutoModelForZeroShotImageClassification.from_pretrained("cringgaard/extrapolation_year_built", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da84d3e8ecc4e047964041065776a8a0b3a8aad685611654f9c4806b0446b1dd
- Size of remote file:
- 5.37 kB
- SHA256:
- 5ee8e48e5cc954c63bc7d4800fb46d419f5746093f2a46c0673114a2cbc321ba
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