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:
- 62e9026ca41b681d6b76345f4aab36fcf4bcd769b262812b7dfefdec4bb7925f
- Size of remote file:
- 471 MB
- SHA256:
- cce170910f4d4f3b45be025508f51ef1ad6a1de69f2d46dce7e4603ad31aaaeb
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