Instructions to use hf-tiny-model-private/tiny-random-Data2VecAudioForSequenceClassification 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-Data2VecAudioForSequenceClassification 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-Data2VecAudioForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForAudioClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecAudioForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecAudioForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d7ba72146b045759afb0edc6ae684de6aca39bb67b8f596a33673af0ac3e911
- Size of remote file:
- 289 kB
- SHA256:
- dec9c557f5595fa9c95b1c75694b6cd8bf4d620c854b0c107190341d76e1d9aa
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