class_docstr: TSV data loader for files having a single input text and a label.
  
  Files must be in TSV format with 2 columns in this order:
  1. Input text.
  2. Numeric label.
  
  Exported examples have 2 output keys: "sentence" and "label".
docstr: Initializes a dataset for the Input Salience Eval demo.
  
  Args:
    path: The path from which examples will be loaded.
    name: Optionally, the name of the dataset. Used by ISEvalModel to
      determine if the model is intended to be compatible with this dataset.
docstr_tok: ["Initializes","a","dataset","for","the","Input","Salience","Eval","demo",".","Args",":","path",":","The","path","from","which","examples","will","be","loaded",".","name",":","Optionally",",","the","name","of","the","dataset",".","Used","by","ISEvalModel","to","determine","if","the","model","is","intended","to","be","compatible","with","this","dataset","."]
code: def __init__(self, path: str, name: str = ""):
      """Initializes a dataset for the Input Salience Eval demo.
  
      Args:
        path: The path from which examples will be loaded.
        name: Optionally, the name of the dataset. Used by ISEvalModel to
          determine if the model is intended to be compatible with this dataset.
      """
      self._examples = self.load_datapoints(path)
      self.name = name
  
file_path: lit/lit_nlp/examples/is_eval/datasets.py
imports: from lit_nlp.api import dataset
  from lit_nlp.api import types
  import pandas
label_desc: Use image node_id 1 to create a new SingleInputClassificationFromTSV object from inherited base classes: lit_dataset with example: obj = SingleInputClassificationFromTSV(path, name)