Text Ranking
sentence-transformers
Safetensors
French
English
xlm-roberta
evalllm2026
cross-encoder
reranker
loss:LambdaLoss
text-embeddings-inference
Instructions to use rarmingaud/evalllm2026-reranker-mesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rarmingaud/evalllm2026-reranker-mesh with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("rarmingaud/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) - Notebooks
- Google Colab
- Kaggle
cea-list-ia/evalllm2026-reranker-mesh
This model was trained by the CEA-LIST to participate in the evalLLM2026 challenge.
Model Description
- Trained by: CEA-LIST
- Task: Entity Linking (MeSH)
- Type: Reranker (Cross-Encoder)
- Training Data: Wikipedia
This reranker is a cross-encoder designed to improve the candidates selected by an embedding model.
Usage
Query Prefix
To use this model, you should prompt it with the following query prefix:
Represent this medical sentence for retrieving relevant MeSH terms:
More Information
For more details, please refer to the GitHub repository.
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