Instructions to use Kx15/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kx15/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kx15/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("Kx15/emotion_classification") model = AutoModelForImageClassification.from_pretrained("Kx15/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kx15/emotion_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/Kx15/emotion_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://Kx15/emotion_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Kx15/emotion_classification/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- c40a887d56e442eb07abae1624ac7ebc84613c624eae77e672b69782b436eef9
- Size of remote file:
- 4.09 kB
- SHA256:
- 44685b6d640783477964bebd9e1cbfc3deefe464194323c0cedb1b0f3df1ceba
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