Text Ranking
sentence-transformers
Safetensors
English
ultrabert
cross-encoder
reranker
Generated from Trainer
dataset_size:1048576
loss:BinaryCrossEntropyLoss
custom_code
Instructions to use cross-encoder-testing/ultrabert-150m-reranker-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder-testing/ultrabert-150m-reranker-test with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("cross-encoder-testing/ultrabert-150m-reranker-test", trust_remote_code=True) query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Commit History
19 days ago
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Simplify model card
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Preserve UltraBERT reranker checkpoint for Transformers integration tests
Verified signatureCopy tomaarsen/ultrabert-150m-reranker-msmarco524k-3e-5-20261014 at 3cec20847f6a8f3d3482444ea08a155bf828abcf. Model files are unchanged. Add test-fixture provenance to the model card.f20e28e -
initial commit
Verified signature1f7881b