Instructions to use cbrew475/mpnet-metric with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cbrew475/mpnet-metric with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cbrew475/mpnet-metric")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cbrew475/mpnet-metric") model = AutoModelForSequenceClassification.from_pretrained("cbrew475/mpnet-metric") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a19563176ffed8cdcc45b33a76387ebc3b2095b03c3a6bbfb926cac12003d96a
- Size of remote file:
- 441 MB
- SHA256:
- 5d5f7a99bc696ec585141e36db427d8a12d947ee8831ba934c12839de8bf7da7
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