class_docstr: The SST dataset.
  
  This dataset is constructed using the Stanford Sentiment Treebank Dataset.
  This dataset contains binary labels (positive or negative) for each sample.
  
  The original dataset specified 5 labels:
  very negative, negative, neutral, positive, very positive with
  the following cutoffs:
  [0, 0.2], (0.2, 0.4], (0.4, 0.6], (0.6, 0.8], (0.8, 1.0]
class_docstr_tok: ["The","SST","dataset",".","This","dataset","is","constructed","using","the","Stanford","Sentiment","Treebank","Dataset",".","This","dataset","contains","binary","labels","(","positive","or","negative",")","for","each","sample",".","The","original","dataset","specified","5","labels",":","very","negative",",","negative",",","neutral",",","positive",",","very","positive","with","the","following","cutoffs",":","[","0",",","0.2","]",",","(","0.2",",","0.4","]",",","(","0.4",",","0.6","]",",","(","0.6",",","0.8","]",",","(","0.8",",","1.0","]"]
code: def __init__(
      self,
      config: DatasetConfig,
      cache_dir: Optional[str] = None,
      include_subtrees=False,
      discard_neutral=False,
      convert_parentheses=True,
      remove_duplicates=False,
  ):
      super().__init__(config, cache_dir=cache_dir)
      self.include_subtrees = include_subtrees
      self.discard_neutral = discard_neutral
      self.convert_parentheses = convert_parentheses
      self.remove_duplicates = remove_duplicates
  
imports: import os
  from typing import List, Optional, Set
  import pandas
  from ludwig.datasets.dataset_config import DatasetConfig
  from ludwig.datasets.loaders.dataset_loader import DatasetLoader
label_desc: Use image node_id 1 to create a new SSTLoader object from inherited base classes: DatasetLoader with example: obj = SSTLoader(config, cache_dir, include_subtrees, discard_neutral, convert_parentheses, remove_duplicates)