Instructions to use hansin91/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hansin91/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hansin91/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("hansin91/emotion_classification") model = AutoModelForImageClassification.from_pretrained("hansin91/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hansin91/emotion_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/hansin91/emotion_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hansin91/emotion_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hansin91/emotion_classification/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 162de4889b75d28ac100f30f639d060f1522c66837fd2918153f8ca51da2b4f4
- Size of remote file:
- 343 MB
- SHA256:
- 07aa0fe7fcd1213cf27b423f0cbcf963ea049f89eb2f721ab2fa008d97c24f7c
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