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:
- f96d458eeeec65e7a97867b6183b8628da6779c6cc2f2a4fa373014908ca7308
- Size of remote file:
- 441 MB
- SHA256:
- 0e0e9c1bbfb0937072ea7f1cb5828117a85dc743eed73ea052bebc67cbd335b3
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