Instructions to use robertsw/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robertsw/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="robertsw/tmp_trainer") 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("robertsw/tmp_trainer") model = AutoModelForImageClassification.from_pretrained("robertsw/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe9d433642c6f7ae57dc7fd180a308e20829b6e374b1ed844efd0f2e4e51af5c
- Size of remote file:
- 347 MB
- SHA256:
- 48a941270203dcdf3d95bfe16e20258e66dea8c2dacf7ca4a6c01cf71f405e62
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