text: Embeddings are dense vector representations that 
  capture the semantics of an input. The first emblem- 
  atic example is Word2Vec, introduced by Mikolov 
  et al. (2013). It consists of neural architectures 
  trained to learn high-quality word representations 
  from contextual relationships in vast amounts of 
  text. Other models were proposed since then, lever- 
  aging the transformer architecture (Vaswani et al., 
  2017) to produce both generic and contextualized 
  word embeddings using self-attention. Many mod- 
  els now exist with various architectures, mono- 
  lingual or multilingual, pre-trained or fine-tuned 
  (Naseem et al., 2021; Ding et al., 2023). 
  
  2 Related Work 
  
  Sentence Embeddings Sentence embeddings are 
  required for many language tasks, such as Semantic 
  Textual Similarity (STS) and knowledge retrieval. 
  Many models have been proposed in the litera- 
  ture, leveraging pooling strategies (Devlin et al., 
  2019; Muennighoff, 2022) or similarity fine-tuning 
  (Reimers and Gurevych, 2019) using a contrastive 
  framework (Gao et al., 2021; Neelakantan et al., 
  2022; Ni et al., 2021; Wang et al., 2022; Zhang 
  et al., 2023), leveraging prompts (Wang et al., 2023) 
  or a two steps training process (Chen et al., 2024; 
  Lee et al., 2024). Few French-language models 
  have been proposed in the literature (Martin et al., 
  
  In this work, our primary objective is to in- 
  troduce a large-scale embedding benchmark for 