Instructions to use jhu-clsp/mmBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhu-clsp/mmBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jhu-clsp/mmBERT-base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jhu-clsp/mmBERT-base", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38494675f12d761fbd669e6694ef227324f5c94c133631e9d1b6250a6bdb3a78
- Size of remote file:
- 1.23 GB
- SHA256:
- 8ea64ec1ea4eb8fca0fc14b69a2ae571de6bfbc25fd214bb932dd4aba6a3a04e
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