Text Classification
Transformers
Safetensors
bert
cross-encoder-reranker
l2
repository-library
repository_library_search_stack
research-library
retrieval
text-embeddings-inference
Instructions to use PeytonT/cross-encoder-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/cross-encoder-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PeytonT/cross-encoder-reranker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PeytonT/cross-encoder-reranker") model = AutoModelForSequenceClassification.from_pretrained("PeytonT/cross-encoder-reranker") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e734103c299ed2cc9faf9ac2fd2fec4a281205de64efe45e77f438eb5e87f7f2
- Size of remote file:
- 5.78 kB
- SHA256:
- 8f38fcbf3ca1d1294efd864ff7fb9a8d947ddb7c48164ae7d78a1494c3b3980e
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