Instructions to use Rossi9/Model_train_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rossi9/Model_train_dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Rossi9/Model_train_dev")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Rossi9/Model_train_dev") model = AutoModelForAudioClassification.from_pretrained("Rossi9/Model_train_dev") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f525c21428ee91dd7e54dcd8956a94b8a24f1eed657ab97a938baed8e42103f9
- Size of remote file:
- 378 MB
- SHA256:
- 0a350c5d8e9a21ed3faa820c7d3dba1a356f1d9dd80c76b4f64b61c1217be5d1
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