Instructions to use khs1218/checkpoint-150 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khs1218/checkpoint-150 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="khs1218/checkpoint-150")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("khs1218/checkpoint-150") model = AutoModelForAudioClassification.from_pretrained("khs1218/checkpoint-150") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50e2579c80a4ca1da326d2c2800717e3784366f061109ca19bcd9578d56e5102
- Size of remote file:
- 757 MB
- SHA256:
- e843c74bda7077263b4b444b60f3eb0b0eaa40b8ff77fa434e225a4e976f9384
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.