Instructions to use Sjdan/cls_3ep1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/cls_3ep1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("Sjdan/cls_3ep1") model = Wav2Vec2ForSpeechClassification.from_pretrained("Sjdan/cls_3ep1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Sjdan/cls_3ep1: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Sjdan/cls_3ep1/resolve/main/training_args.bin
- Command line
-
hf download hf://Sjdan/cls_3ep1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sjdan/cls_3ep1/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 154fb77a7557c2ac977780f8fce361d8defe0c2619f7b597475814595cd935b1
- Size of remote file:
- 3.58 kB
- SHA256:
- 29de3ab1390b8925f3edb9088992c32edef376c92e59df70e7ac35460528d9df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.