class_docstr: Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
  
  Default values expects a tab separated file with the first & second column the sentence pair and third column the score (0...1). Default config normalizes scores from 0...5 to 0...1
docstr: filename specified which data split to use (train.csv, dev.csv, test.csv).
code: def get_examples(self, filename, max_examples=0):
      """
      filename specified which data split to use (train.csv, dev.csv, test.csv).
      """
      filepath = os.path.join(self.dataset_folder, filename)
      with gzip.open(
          filepath, "rt", encoding="utf8"
      ) if filename.endswith(".gz") else open(
          filepath, encoding="utf-8"
      ) as fIn:
          data = csv.reader(
              fIn, delimiter=self.delimiter, quoting=self.quoting
          )
          examples = []
          for id, row in enumerate(data):
              score = float(row[self.score_col_idx])
              if self.normalize_scores:  # Normalize to a 0...1 value
                  score = (score - self.min_score) / (
                      self.max_score - self.min_score
                  )
  
              s1 = row[self.s1_col_idx]
              s2 = row[self.s2_col_idx]
              examples.append(
                  InputExample(
                      guid=filename + str(id),
                      texts=[s1, s2],
                      label=score,
                  )
              )
  
              if max_examples > 0 and len(examples) >= max_examples:
                  break
  
      return examples
  
file_path: sentence-transformers/sentence_transformers/readers/STSDataReader.py
imports: from .None import InputExample
  import csv
  import gzip
  import os
label_desc: Use image node_id 2 for calling the STSDataReader obj's underlying member method code with example usage: obj.get_examples(filename, max_examples) and returns: examples