Instructions to use ndavidson/reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndavidson/reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndavidson/reranker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ndavidson/reranker") model = AutoModelForSequenceClassification.from_pretrained("ndavidson/reranker") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50f2c41fe286aa384c8af8c89592db23d5150c04a2b756d80a8977dcbffef607
- Size of remote file:
- 4.92 kB
- SHA256:
- 2cbf70232036733fb3e6e678055d099760eade07cedf5bdfdb22fcbf30e444d7
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