text: The datasets correlation w.r.t model ranking are 
  presented in appendix Figure 12. Except for 
  two datasets (MasakhaNEWSClusteringP2P, Sum- 
  mEvalFr), the correlations, on average, are high. 
  There is still enough diversity to make each dataset 
  interesting for the French MTEB benchmark. Two 
  groups (SyntecReranking/ SyntecRetrieval, Mas- 
  siveScenarioClassification/ MTOPDomainClassi- 
  fication/ MassiveIntentClassification) exhibit no- 
  tably high correlations (∼0.97). It is interesting 
  to point out some sub-diagonal correlation blocks. 
  The datasets being arranged by task indicate that 
  models behave slightly more similarly within the 
  same task than between two different tasks. This 
  underscores the importance of having multiple 
  tasks in the benchmark to select general-purpose 
  models. For readers interested in specific tasks, 
  it is more relevant to examine task-specific rank- 
  ings rather than the overall one. The complemen- 
  tary results of model correlations w.r.t to strengths 
  and weaknesses on datasets are displayed in ap- 
  pendix Figure 11. Strong correlations in behavior 
  emerge among the variants of the same models 
  (e.g. DistilBERT, sentence-croissant, sentence-t5, 
  e5, etc.). Correlations are also generally observed 
  among numerous models trained using the sentence 
  transformers framework (Reimers and Gurevych, 
  2019), as well as proprietary models, e.g. from 
  Cohere and OpenAI. Conversely, these models fine- 
  tuned for sentence similarity, show minimal cor- 
  relation with pre-trained models for which token- 
  embedding pooling techniques are employed. 