Text Classification
sentence-transformers
Safetensors
English
bert
cross-encoder
reranker
retrieval
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use matulichpt/radlit-crossencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use matulichpt/radlit-crossencoder with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("matulichpt/radlit-crossencoder") 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
File size: 134 Bytes
28114b3 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:3dfc8832e0d99ed4c39d357bd5be9ea2552eab7107daa09b30db39a43f741a73
size 133464836
|