text: 3.1.2 HAL (Clustering) 
  
  Hyper Articles en Ligne (HAL) is a French open 
  archive of scholarly documents from all academic 
  fields. Scrapping this resource, we fetched 85,000 
  publications in French5. We extracted IDs, titles 
  and the author’s choice among domain labels. The 
  last 2 are provided by authors when submitting 
  their papers to HAL. Since domain annotations are 
  provided, the dataset can be used for many tasks, 
  such as topic modeling or text classification. To en- 
  sure the dataset quality is suitable for a benchmark, 
  further data cleaning has been performed: 
  
  3.1 New Datasets 
  
  We identified 7 datasets relevant to French in the ex- 
  isting MTEB, which we assume are of good quality. 
  We complemented these with 8 external relevant 
  datasets proposed in the literature, such as BSARD 
  (Louis and Spanakis, 2022) and Alloprof (Lefebvre- 
  Brossard et al., 2023), which are proven to be good 
  quality. We created 3 new ones presented in Table 1 
  and assessed their quality with various procedures 
  and metrics. In addition to all performed checks, 
  we run multiple models on these datasets and pro- 
  vide results to show that they are neither trivial nor 
  impossible to solve (see Tables 10, 11, 12 and 13). 
  Therefore, as of today, our French MTEB 
  runs on 18 datasets. Some datasets are framed 
  differently according to the task category they 
  are used with. For example, MasakhaNEWS 
  dataset 
  is used for 
  both Classification (MasakhaNEWSClassification) 
  and Clustering (MasakhaNEWSClusteringS2S and 