Instructions to use hf-internal-testing/tiny-random-SEWDForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SEWDForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-SEWDForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SEWDForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-SEWDForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 446424d8685dbac59c0af25967990a03da2193f9214e313a7cccd3fbf6321145
- Size of remote file:
- 307 kB
- SHA256:
- dfc7ef883d7629f6697ba4cd4c0a131c50fb0ab02f05e403437f341070d9fe9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.