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 pytorch_model.bin from aditira/emotion_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/aditira/emotion_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://aditira/emotion_classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aditira/emotion_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- dc9ba95d5fc2fc2ff0e2cc52740bae849e267e0481828a39752c8687aff95af1
- Size of remote file:
- 343 MB
- SHA256:
- f9f37826314e405945386c317c12739adb4e14e454816ce18ec7f0f72252d518
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