Instructions to use LastTransformer/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LastTransformer/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="LastTransformer/results") 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("LastTransformer/results") model = AutoModelForImageClassification.from_pretrained("LastTransformer/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- efb546cadf8e308aaf7bdea987a703c03eed828b680ad9bc39850f4715f2c9a9
- Size of remote file:
- 5.3 kB
- SHA256:
- 2a0f94c4e52767662d6004e6bbba20a5fb159a67533d2d90ee27d904736e4349
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