Instructions to use rfhuang/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rfhuang/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="rfhuang/results")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("rfhuang/results") model = AutoModelForAudioClassification.from_pretrained("rfhuang/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c027690ee789e7b46e9318ee28f3f916c353d25c856cbe27db0e8492f31b9ce3
- Size of remote file:
- 5.05 kB
- SHA256:
- f8ebb5c074c4e876e9fc8efe315a3c92065e285f90c78f90ff52120898cba067
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