Instructions to use hilmansw/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hilmansw/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hilmansw/emotion_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hilmansw/emotion_classification") model = AutoModelForImageClassification.from_pretrained("hilmansw/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hilmansw/emotion_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/hilmansw/emotion_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hilmansw/emotion_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hilmansw/emotion_classification/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- cdbc0e79781b115f9b7e0d5e1952612a0241b39a42d48b11176caf60e4a730fb
- Size of remote file:
- 343 MB
- SHA256:
- 2319008b33e455b487d3247a877003cf63b0aae54ef3e57f16ed995f98503a50
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