Instructions to use heado/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heado/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="heado/my_model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("heado/my_model") model = AutoModelForAudioClassification.from_pretrained("heado/my_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bc3945711f4fdd383065004051114cfb83a3d11dea8af92c93026d64cddee3d
- Size of remote file:
- 5.78 kB
- SHA256:
- 7168530a7c0a7f869fc6d929f49e9489370fc2f9dfec5b6f48f994d28de6de1c
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