text: Figure 1 shows a global model comparison 
  across all datasets. The models are arranged hori- 
  zontally according to their performance, with the 
  best models on the left. The black bars repre- 
  sent the statistical equivalence between the mod- 
  els’ performances. The statistically equivalent 
  top performers for this benchmark are OpenAI’s 
  models text-embedding-3-large, text-embedding-3- 
  small and text-embedding-ada-002. Interestingly, 
  many models do not show a significant perfor- 
  mance gap between their base and large flavours. 
  Some French models stand out among the multi- 
  lingual models, such as Solon-embeddings-large- 
  0.1, sentence_croissant_alpha_v0.3 and sentence- 
  camembert-large. 
  Furthermore, we observe a performance correla- 
  tion with the embedding dimension and the model’s 
  number of parameters, which are often correlated 
  themselves. This appears very clearly on the rela- 
  tive ranking of E5 and T5 models (see Figure 1). 
  However, some small models perform very well 
  on the benchmark, such as the standard version 
  of the multilingual universal sentence encoder or 
  Solon-embeddings-base-1.0. Notably, the maxi- 
  mum sequence length, while an important criterion 
  for generative tasks with LLMs, is less correlated 
  with performance than the other dimensions. This 
  can be explained by many datasets containing rel- 
  atively small texts (see appendix Table 3 showing 
  that 14 datasets have less than 50 tokens). 