Text Ranking
sentence-transformers
PyTorch
Safetensors
Korean
electra
sentence_transformers
cross_encoder
Instructions to use ddobokki/electra-small-sts-cross-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ddobokki/electra-small-sts-cross-encoder with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("ddobokki/electra-small-sts-cross-encoder") 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
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Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers` (#2)
Verified signature- Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers` (f3ae3220bd4ead0b24e5ce0968a8bcf7f915a2a1) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>e7e16e5
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Adding `safetensors` variant of this model (#1)
Verified signature- Adding `safetensors` variant of this model (e49830170f19085db782678c822d20fd6451c11b) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>a9bd9eb
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