Instructions to use al-css/PagesClassificationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use al-css/PagesClassificationModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="al-css/PagesClassificationModel") 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("al-css/PagesClassificationModel") model = AutoModelForImageClassification.from_pretrained("al-css/PagesClassificationModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3299478318dddf0e819ccb61b22d4460b24a13d8138e61f8ef9ab852591806a5
- Size of remote file:
- 5.18 kB
- SHA256:
- 3506de44e141d123f60811d022e10212b5340a5079bec55943aa321642141efd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.