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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e77168548f21da0a0de31c97dc92b5878c3c3b8b3b332026e2e2e3699ff9837a
- Size of remote file:
- 135 kB
- SHA256:
- 99d077c2de608feea34e7b89c9e10d6bc9ae607975fbef7f865f339fd0b0d4d7
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