text: tation and, in practical applications, the under- 
  lying storage and compute costs. We selected 
  models with embedding dimensions ranging 
  from 384 to 4096. 
  
  et al., 2019), their variants optimized for semantic 
  similarity (Reimers and Gurevych, 2019), numer- 
  ous multilingual models performing at the top on 
  MTEB (e.g E5 and T5), Bloom variants (Zhang 
  et al., 2023), models based on very recent power- 
  ful LLMs (Wang et al., 2023; Faysse et al., 2024) 
  and finally the proprietary models of OpenAI, Co- 
  here and Voyage. Certain models were selected in 
  multiple sizes to isolate the dimensionality effect 
  effectively. We provide information on the mod- 
  els’ licenses as reported in the Hugging Face hub10. 
  However, we encourage readers to conduct further 
  research before utilizing a model. 
  • Sequence length: Being the number of to- 
  kens that a model can consider as input, the 
  sequence length is important as it impacts the 
  unit that can be encoded (sentence, paragraph, 
  document). However, encoding overly long 
  sequences requires efficiently storing the rele- 
  vant information into a single vector. Among 
  the selected methods, this criterion varies 
  from 128 tokens to 32768. 