text: the other models. However, other models should be 
  considered for their performance on specific tasks, 
  being open source or having a small embedding 
  dimension. 
  
  This work opens several doors for future im- 
  provements. By examining dataset diversity in 
  terms of topics and model ranking, we observe 
  that the benchmark would benefit from additional 
  datasets that introduce higher diversity. Beyond 
  classification, many tasks focus on semantic simi- 
  larity, explaining the strong performance of models 
  trained for similarity. Exploring novel tasks in the 
  generative spectrum or evaluating token embed- 
  dings (contextualized or not) on tasks like Named 
  Entity Recognition could be an interesting path 
  for future exploration. There are also opportuni- 
  ties for improvements on the model side. With 
  numerous existing models that could be added to 
  the leaderboard and many new proposals awaiting. 
  For instance, we can already see the promising ca- 
  pabilities of early variants of recent models (Faysse 
  et al., 2024) and expect that future proposals will 
  come to compete strongly with closed-source mod- 
  els. Ultimately, we hope to see the emergence of 
  other language-specific MTEB variants (e.g. for 
  high-resource languages like Spanish and German), 
  enabling a more comprehensive evaluation of mul- 
  tilingual model performance. 