Instructions to use hf-tiny-model-private/tiny-random-Data2VecAudioModel 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-Data2VecAudioModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-Data2VecAudioModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecAudioModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecAudioModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5fc2788172d4a3239f9a2143b97ea19e72ad06d9a51e8869dc8fe14d4c5a6fc6
- Size of remote file:
- 267 kB
- SHA256:
- 79ef33aa9f1b68c2a423ef26ff1930fe99c84ae418cd3d27644c0af34611db13
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