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:
- 0a9f1582d8fe1b445ec4b0944df8fae2f26ff49dcb38f1fe4c9db9f7ce86e1c3
- Size of remote file:
- 343 MB
- SHA256:
- f3418fd789284bd748b25014eb04f529c1b25ccced75d6732ce9aa3f777ca61e
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