Feature Extraction
Transformers
PyTorch
hear_canon_vit
audio
medical
embeddings
vision-transformer
distillation
canon
custom_code
Instructions to use matthewagi/HeAR-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matthewagi/HeAR-s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="matthewagi/HeAR-s", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("matthewagi/HeAR-s", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c63a9173b6e8771688948321a893feab25d38b5b55f6b16c8a7b157220d383c6
- Size of remote file:
- 88.7 MB
- SHA256:
- 93683609dd87a36c97dd1a7e47baa4fa50144c5acacf29eceeb0565323c5d8b4
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