Instructions to use aditira/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aditira/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aditira/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("aditira/emotion_classification") model = AutoModelForImageClassification.from_pretrained("aditira/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aditira/emotion_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/aditira/emotion_classification/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://aditira/emotion_classification@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aditira/emotion_classification/resolve/refs%2Fpr%2F1/training_args.bin
4.09 kB
- Xet hash:
- 0353dc1d57c4a5c6ae18e63e47972f98e732bcc45d1a04b5ac6abc4368e5f309
- Size of remote file:
- 4.09 kB
- SHA256:
- 077e1dd1f98c724b70884493bc5082d517a13251a937d9f107395b176e7925ae
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