Instructions to use universalml/we with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/we with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/we") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("universalml/we") model = AutoModelForImageClassification.from_pretrained("universalml/we") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4fefa2d863681067779a1b0db7004b14df7d568c14e5316a6e386459bfa5238
- Size of remote file:
- 4.16 kB
- SHA256:
- c2c7b0664e820972bb5e9bda132828d2d6e8e1f31dd8a67e9323d429e32d891c
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