EvalLLM2026-EL
Collection
Models trained by the CEA-LIST to participate in the EvalLLM2026-EL challenge • 8 items • Updated • 1
How to use cea-list-ia/evalllm2026-reranker-mesh with sentence-transformers:
from sentence_transformers import CrossEncoder
model = CrossEncoder("cea-list-ia/evalllm2026-reranker-mesh")
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)This model was trained by the CEA-LIST to participate in the evalLLM2026 challenge.
This reranker is a cross-encoder designed to improve the candidates selected by an embedding model.
To use this model, you should prompt it with the following query prefix:
Represent this medical sentence for retrieving relevant MeSH terms:
For more details, please refer to the GitHub repository.
Base model
BAAI/bge-m3