Instructions to use hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForAudioClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62b5ca21b18a34c1bb3102a6f25b3e4dd604b36ab9f7a6f4ab109fe31d0552a5
- Size of remote file:
- 484 kB
- SHA256:
- 836516c970bd633c4420ec5e51cad6d62c33ecaff0b414b60d355633587bac85
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