content: from whatlies import Embedding, EmbeddingSet
  from whatlies.language._common import SklearnTransformerMixin
  from sentence_transformers import SentenceTransformer
  
  
  class SentenceTFMLanguage(SklearnTransformerMixin):
      """
      This class provides the abitilty to load and use the encoding strategies found in the [sentence transformers](https://www.sbert.net/index.html) library.
  
      A full list of pretrained embeddings can be found [here](https://www.sbert.net/docs/pretrained_models.html).
  
      Arguments:
          name: name of the model to load, be sure that it's downloaded or trained beforehand
  
      **Usage**:
  
      ```python
      > from whatlies.language import SentenceTFMLanguage
      > lang = SentenceTFMLanguage('distilbert-base-nli-stsb-mean-tokens')
      > lang['python is great']
      > lang[['python is a language', 'python is a snake', 'dogs are cool animals']]
      ```
      """
  
      def __init__(self, name):
          self.name = name
          try:
              self.model = SentenceTransformer(name)
          except OSError:
              raise ValueError(
                  f"Make sure that the name (got {name}) is found on this website:\nhttps://www.sbert.net/docs/pretrained_models.html"
              )
  
      def __getitem__(self, query):
          if isinstance(query, str):
              return Embedding(query, vector=self.model.encode(query))
          else:
              return EmbeddingSet(*[self[tok] for tok in query])
  