Instructions to use anderloh/HuggingfaceBest5ClassModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/HuggingfaceBest5ClassModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/HuggingfaceBest5ClassModel")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/HuggingfaceBest5ClassModel") model = AutoModelForAudioClassification.from_pretrained("anderloh/HuggingfaceBest5ClassModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62964c188263050e8e45aa2f3a75f159e90e9f0dc61aa217713830a43bac1e12
- Size of remote file:
- 95.9 MB
- SHA256:
- bd83ae850a6f38e955a9a0ecf3728e21f005733ae6ae7f948544118441a4714b
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