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:
- 0606c614534b1ee9ab664f970336253cb189eb0b109f1556f91116350350986d
- Size of remote file:
- 133 MB
- SHA256:
- 91bf578a5016c3e806254d239066ffebb0356ffc6d1b521cdcc858f77344e4d3
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