Instructions to use RavenOnur/Sign-Language with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RavenOnur/Sign-Language with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RavenOnur/Sign-Language") 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("RavenOnur/Sign-Language") model = AutoModelForImageClassification.from_pretrained("RavenOnur/Sign-Language", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4eb7d76c4b827e3ee3e46b44bda82ce644fd791d308b5b441f6fd6c4ed344d42
- Size of remote file:
- 343 MB
- SHA256:
- aa6e3bfb2757e4ac00707c4e1a0d0917e3abbe525becb1add22dcb3a2aba4f8a
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