Sentence Similarity
sentence-transformers
Safetensors
French
English
xlm-roberta
evalllm2026
text-embeddings-inference
Instructions to use rarmingaud/evalllm2026-mesh-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rarmingaud/evalllm2026-mesh-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rarmingaud/evalllm2026-mesh-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] - Notebooks
- Google Colab
- Kaggle
cea-list-ia/evalllm2026-mesh-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 Model)
- Training Data: Wikipedia, further trained on the training data of the challenge.
This retriever is an embedding model designed to find the closest matches in MeSH for entity linking.
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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Model tree for rarmingaud/evalllm2026-mesh-finetuned
Base model
BAAI/bge-m3