Instructions to use atarensi/test_helloworldModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atarensi/test_helloworldModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="atarensi/test_helloworldModel") 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("atarensi/test_helloworldModel") model = AutoModelForImageClassification.from_pretrained("atarensi/test_helloworldModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 827fc637d4b2e62e99d75e3b66b87e70a18c01fdf2b9240125bf2a09908993a1
- Size of remote file:
- 343 MB
- SHA256:
- 8fb5f7703115de0c0c15e04616bcfcf01d5dba29023a829fb68b5bc82533a75b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.