Instructions to use selinawisco/result_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selinawisco/result_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="selinawisco/result_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("selinawisco/result_2") model = AutoModelForAudioClassification.from_pretrained("selinawisco/result_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from selinawisco/result_2: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/selinawisco/result_2/resolve/main/training_args.bin
- Command line
-
hf download hf://selinawisco/result_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/selinawisco/result_2/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 0e7ff7bda6a9f4645d897892323851ec856ef55246a0f66e24fdb196b97b7833
- Size of remote file:
- 4.03 kB
- SHA256:
- 94d2e54f2dc5a62b1c1977b35bbc4dd6290cf01d31b823bd5fc0e6fd1339cbad
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