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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ab2df1b12db1af95644aba928b3c6268f8f33c55681a0fbfbc009b7adc848a1
- Size of remote file:
- 133 MB
- SHA256:
- bcc275fe3e183a68e629c55355be77ef51c2ee780990173ca8f8edc65152fae7
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