cea-list-ia/evalllm2026-geonames-finetuned
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: Retriever (Embedding)
- Training Data: Wikipedia, further trained on the training data of the challenge.
This retriever is an embedding models designed to find the closest match in GeoNames for entity linking.
Usage
Query Prefix
To use this model, you should prompt it with the following query prefix:
Represent this geographical sentence for retrieving relevant GeoNames terms:
More Information
For more details, please refer to the GitHub repository.
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Model tree for rarmingaud/evalllm2026-geonames-finetuned
Base model
BAAI/bge-m3
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rarmingaud/evalllm2026-geonames-finetuned") 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]