Instructions to use voidful/hubert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/hubert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="voidful/hubert-tiny")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("voidful/hubert-tiny") model = AutoModel.from_pretrained("voidful/hubert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c531c10007de70cc927b31119e960a91fad300322f71933302525659faa90ebe
- Size of remote file:
- 65.6 MB
- SHA256:
- 97cce607ce1d2a01dda6deca8b1398eaa2d10d52cc28d4d8fae9bfb6b711cf4f
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