Instructions to use anderloh/HuggingfaceTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/HuggingfaceTest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/HuggingfaceTest")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/HuggingfaceTest") model = AutoModelForAudioClassification.from_pretrained("anderloh/HuggingfaceTest") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f86212e0cb7ca990df31c2cd926c77d97d2095cc55ec42a457418f193a1b17d
- Size of remote file:
- 52.2 MB
- SHA256:
- 429195c8c73df7eab4c2c253119a3c88a67a1708c472bece1c0b063064fa1fdb
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