Instructions to use selinawisco/result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selinawisco/result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="selinawisco/result")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("selinawisco/result") model = AutoModelForAudioClassification.from_pretrained("selinawisco/result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from selinawisco/result: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/selinawisco/result/resolve/main/training_args.bin
- Command line
-
hf download hf://selinawisco/result/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/selinawisco/result/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 20c8441c986bd5a7e426e443fdec3c3f1452f0774f48c01b506666c7b752b03a
- Size of remote file:
- 3.96 kB
- SHA256:
- 3c25f61b6c60deb51dd4d127608930e4b2b2decddc4b84b74e7b5032afd34849
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