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:
- d09024e7eeea3f4ef232096023ec8d9fa564f147a373bb19388419d7f09260f6
- Size of remote file:
- 441 MB
- SHA256:
- f6e114ec5549d08c13a52583e67328396e4933b5a53e27736567aed5b10bcf21
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