Instructions to use voidful/mhubert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/mhubert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="voidful/mhubert-base")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("voidful/mhubert-base") model = AutoModel.from_pretrained("voidful/mhubert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0a6ede941e2ef3244211835f363db715294a11a21eacd31e30bad8c87b34a3b
- Size of remote file:
- 378 MB
- SHA256:
- 1cf1ee3936d0a7ecdecee25aca31dd2ce8fc20b78bf86ade902300456a51e2b9
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