Instructions to use hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 011e04f34eca0c1b8ce47ff6a6ef4c7a03ddc9f1e31e5e49d4a5e0ec44450f3a
- Size of remote file:
- 153 kB
- SHA256:
- f610013373da3cf869dd6ae6e384a8664e3bf99d10451ed1cfe43974e0d55cb0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.