Instructions to use anderloh/HuggingfaceBest5ClassModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/HuggingfaceBest5ClassModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/HuggingfaceBest5ClassModel")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/HuggingfaceBest5ClassModel") model = AutoModelForAudioClassification.from_pretrained("anderloh/HuggingfaceBest5ClassModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 093e482fae343b10da8d68775bb21fe4300a019f76e6e6ee5b28cbbd7c10a679
- Size of remote file:
- 1.06 kB
- SHA256:
- 66b1ff4c92709ee514d60150ad1c67a13e001dac71642548c74f443c1156d358
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.