Instructions to use NekoJar/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NekoJar/trainer_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="NekoJar/trainer_output") 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("NekoJar/trainer_output") model = AutoModelForImageClassification.from_pretrained("NekoJar/trainer_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69957361063394d3feb8a3188193ef6d7a45fda446a70e40252329675c6b9f92
- Size of remote file:
- 5.3 kB
- SHA256:
- 71b5e36a01fd79d800e3710cdfb03ae80a116ca1ae1626c678448287128ee56a
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