text: The reranking task, as evaluated in MTEB, requires 
  datasets composed of a set of queries, each as- 
  sociated with relevant and irrelevant documents. 
  Despite our efforts, we found no French dataset 
  that natively exhibits such a structure. Thus, to 
  evaluate this task, we built data for the reranking 
  task based on the Syntec and Alloprof (Lefebvre- 
  Brossard et al., 2023) datasets. These already fea- 
  ture queries and labeled relevant documents. Irrele- 
  vant ones were added using the following process: 
  
  3.2 Models 
  
  For comparison on our benchmark, we selected 
  various models to fulfil three objectives. 
  
  It was imperative to include 
  top performers from the MTEB benchmark 
  (Muennighoff et al., 2022). We mainly se- 
  lected multilingual models and some English 
  models to asses their language-transferring 
  abilities. Additionally, we integrated natively 
  French transformer-based models such as 
  CamemBERT (Martin et al., 2019), FlauBERT 
  (Le et al., 2020) and even the very recent 
  CroissantLLM (Faysse et al., 2024). 