Instructions to use CreeperStone72/mood_box with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CreeperStone72/mood_box with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="CreeperStone72/mood_box")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("CreeperStone72/mood_box") model = AutoModelForAudioClassification.from_pretrained("CreeperStone72/mood_box", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from CreeperStone72/mood_box: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/CreeperStone72/mood_box/resolve/main/training_args.bin
- Command line
-
hf download hf://CreeperStone72/mood_box/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/CreeperStone72/mood_box/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- ecadeb1ca438198c5f767e7c0fddabaad08d6701cea40d88fee760bf0cab68a8
- Size of remote file:
- 4.86 kB
- SHA256:
- e09fd5ceb12419a78f864bc1103119318250d991c5a74b441ec6303a9e6145c2
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