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:
- 49346ef263074fc988ad73993bf9a8cdd6978fc016fdd44bf3667876966420a2
- Size of remote file:
- 65.6 MB
- SHA256:
- 55ca060fd3439cc20b8477d8b2378793dcf35d7b9c74fcc4fe02f336743aa43f
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