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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09f5a9c328e111ebe3451a42e321ba3d30eae200e501c90315c1985dc852011c
- Size of remote file:
- 441 MB
- SHA256:
- c2ffecffe2686fbfcf6c605f19a76866c3460b37457230cdb8160464fddb4b2f
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