Instructions to use anderloh/PushToHubModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anderloh/PushToHubModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anderloh/PushToHubModel")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anderloh/PushToHubModel") model = AutoModelForAudioClassification.from_pretrained("anderloh/PushToHubModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebc599ee2af8eaa4ef20c48289eb7814a5ab56108d7299f27a10b815622027f6
- Size of remote file:
- 4.98 kB
- SHA256:
- aa4918879661aa69e51b293af375106eaef4123a72b66b74de7059a8f846dcf0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.