Instructions to use nreimers/MiniLM-L6-H384-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nreimers/MiniLM-L6-H384-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nreimers/MiniLM-L6-H384-uncased")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nreimers/MiniLM-L6-H384-uncased") model = AutoModel.from_pretrained("nreimers/MiniLM-L6-H384-uncased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2299d4584298eed85eb10de2cc067e059e1134a10b554f206b5661feb6cf41ea
- Size of remote file:
- 90.9 MB
- SHA256:
- ab5048f3effe06bccb318ee923df90cba4e918b309a0ac6b00c654ede0768b87
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