Instructions to use dragonities/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dragonities/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dragonities/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("dragonities/results") model = AutoModelForImageClassification.from_pretrained("dragonities/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 637be9c0fb8d71fef3742cec460ab492931808839a3824bbd6aba2bc5483f89f
- Size of remote file:
- 5.24 kB
- SHA256:
- 9416834f367a78ab1e078d060364548095fa9c51b942b51e6061c78adbc1b13b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.