Instructions to use razent/spbert-mlm-zero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/spbert-mlm-zero with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="razent/spbert-mlm-zero")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("razent/spbert-mlm-zero", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76d549f2e1323097f42a94ce77b9c86bceffbbfb11e2518245f033f3180880a4
- Size of remote file:
- 436 MB
- SHA256:
- 70fe05725244ec670c5f35d132ea21560d497cb64d4d22d48d24910e440d67ad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.