Instructions to use midoiv/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midoiv/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="midoiv/results")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("midoiv/results") model = AutoModelForAudioClassification.from_pretrained("midoiv/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 750a26e8d45b6c7fdd962f47f9504bd570ac89a031debe4ef163c95226938011
- Size of remote file:
- 3.06 kB
- SHA256:
- 7e2b6afc5bcdc3a1c9bcf583f96535f3f757d1103032c2a4649ae57770815bc1
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