token_indexers: Dict[str,] = None,
    use_subtrees: bool = False,
    granularity: str = '5-class',
) -> None

Reads tokens and their sentiment labels from the Stanford Sentiment Treebank.

The Stanford Sentiment Treebank comes with labels from 0 to 4. "5-class" uses these labels as is. "3-class" converts the problem into one of identifying whether a sentence is negative, positive, or neutral sentiment. In this case, 0 and 1 are grouped as label 0 (negative sentiment), 2 is converted to label 1 (neutral sentiment) and 3 and 4 are grouped as label 2 (positive sentiment). "2-class" turns it into a binary classification problem between positive and negative sentiment. 0 and 1 are grouped as the label 0 (negative sentiment), 2 (neutral) is discarded, and 3 and 4 are grouped as the label 1 (positive sentiment).

Expected format for each input line: a linearized tree, where nodes are labeled by their sentiment.

The output of read is a list of Instance s with the fields: tokens : TextField and label : LabelField

Registered as a DatasetReader with name "sst_tokens".


  • token_indexers : Dict[str, TokenIndexer], optional (default={"tokens": SingleIdTokenIndexer()})
  • We use this to define the input representation for the text. See :class:TokenIndexer.
  • use_subtrees : bool, optional, (default = False) Whether or not to use sentiment-tagged subtrees.
  • granularity : str, optional (default = "5-class") One of "5-class", "3-class", or "2-class", indicating the number of sentiment labels to use.


    tokens: List[str],
    sentiment: str = None,
) ->

We take pre-tokenized input here, because we don't have a tokenizer in this class.


  • tokens : List[str], required. The tokens in a given sentence.
  • sentiment : str, optional, (default = None). The sentiment for this sentence.


AnInstancecontaining the following fields: tokens: TextField The tokens in the sentence or phrase. label: LabelField The sentiment label of the sentence or phrase.