Instructions to use microsoft/MiniLM-L12-H384-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/MiniLM-L12-H384-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/MiniLM-L12-H384-uncased")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/MiniLM-L12-H384-uncased", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9fef316b81be188ae7b4e570cf2839966f566f62ce53785cdae41ba3a003743
- Size of remote file:
- 134 MB
- SHA256:
- 35d6b00bb8476fbed6ee473f36c9269b3cd7a28394b32f90b132c06caa3051e4
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