Instructions to use selinawisco/APAC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selinawisco/APAC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="selinawisco/APAC")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("selinawisco/APAC") model = AutoModelForAudioClassification.from_pretrained("selinawisco/APAC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from selinawisco/APAC: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/selinawisco/APAC/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://selinawisco/APAC/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/selinawisco/APAC/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 38522b9e0c3ee15cf45b6dd6a83b979613431ebcd8c02254e34d37b9c4af66c1
- Size of remote file:
- 378 MB
- SHA256:
- 82ff84b7715bf139725102f8f39342ec3ecd2151c07fdfa282b29e818eed9ec8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.