text: """
  # Models
  
  Submodules containing the models.
  
  
  ## Quick overview
  
  For now, models only contains a simple wrapper for gensim's implementation of word2vec.
  
  Its main advantage is to be directly compatible with our implementation of the preprocessing.
  
  The Word2Vec model takes as an input :
  
  - A bunch of preprocessed files, represented as a list of path, or the path of a directory containing plain, raw text files.
  
  - The parameters of the model
  
  The model has 3 main outputs :
  
  - An ```embeddings.tsv``` file, containing one embedding per line, with each coordinates separated by a '\t' token. 
  
  - ```A metadata.tsv``` file, containing 1 word per line, each line corresponding to the embedding on the same line in ```embeddings.tsv```. Warning : the first line of this file has a header, that the ```embeddings.tsv``` file doesn't have, resulting in a shift of one line in the ```embeddings.tsv``` - ```A metadata.tsv``` correspondence.
  
  - A word2vec.model file with the trained gensim checkpoints to restore the model from. 
  
  ## Usage example
  
  ```python
  from embeddingsprep.models.word2vec import Word2Vec
  model = Word2Vec(emb_size=300, window=5, epochs=3)
  model.train('./my-preprocessed-data/')
  model.save('./my-output-dir')
  ```
  """
  