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:
- a71a5b62852efb110d68c3647f69711a66332e8e48de0fb2534e29a884e97973
- Size of remote file:
- 52.2 MB
- SHA256:
- 12b72afca351d5838b740f0ec6003c2e1e2a8c0f5156e6629ad3e2ef735bb540
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