Instructions to use microsoft/Multilingual-MiniLM-L12-H384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Multilingual-MiniLM-L12-H384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/Multilingual-MiniLM-L12-H384")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/Multilingual-MiniLM-L12-H384", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9598f283c600826bfc07c4a85be7398a86785dad32130dab552f1047a3199c0
- Size of remote file:
- 471 MB
- SHA256:
- e5293741fa476159c33cfd82bdbb89229e096c6980790524de3a0e45ba222e09
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