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-mesh with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("cea-list-ia/evalllm2026-mesh")
sentences = [
"C'est une personne heureuse",
"C'est un chien heureux",
"C'est une personne très heureuse",
"Aujourd'hui est une journée ensoleillée"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]This model was trained by the CEA-LIST to participate in the evalLLM2026 challenge.
This retriever is an embedding models designed to find the closest match in MeSH for entity linking.
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