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 pytorch_model.bin from selinawisco/result: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/selinawisco/result/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://selinawisco/result/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/selinawisco/result/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 29ceb58873007d9f493aa8c6e8b4946fdcdce17eb2a890bbea840d981c2b7296
- Size of remote file:
- 378 MB
- SHA256:
- ef26b709019b8a5fcee6cee04f2e7672a6d01c66a24ea00dc9d26ef78ba13204
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