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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1547832deb2a7b99b8c381357149d1a4caf8b3570697c6b232bf799004cad20c
- Size of remote file:
- 289 kB
- SHA256:
- 3cbb26e9a57b44d3008fac386903d5d94bc194144f3d16a94a575726a799c8bd
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