Instructions to use hf-tiny-model-private/tiny-random-HubertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-HubertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-tiny-model-private/tiny-random-HubertForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-HubertForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-HubertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96f0c294f2808d0ece19d9ba32c620fa4986e97ce389dc470ad3283c69f4a76d
- Size of remote file:
- 153 kB
- SHA256:
- c94fa1d1c9efc2f99edf31518bbc1b328bfc2ee4e96bd5bdec41483e8b4eef85
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