class_docstr: This evaluator can be used with the CrossEncoder class. Given sentence pairs and continuous scores,
  it compute the pearson & spearman correlation between the predicted score for the sentence pair
  and the gold score.
code: def __call__(
      self,
      model,
      output_path: str = None,
      epoch: int = -1,
      steps: int = -1,
  ) -> float:
      if epoch != -1:
          if steps == -1:
              out_txt = " after epoch {}:".format(epoch)
          else:
              out_txt = " in epoch {} after {} steps:".format(
                  epoch, steps
              )
      else:
          out_txt = ":"
  
      logger.info(
          "CECorrelationEvaluator: Evaluating the model on "
          + self.name
          + " dataset"
          + out_txt
      )
      pred_scores = model.predict(
          self.sentence_pairs,
          convert_to_numpy=True,
          show_progress_bar=False,
      )
  
      eval_pearson, _ = pearsonr(self.scores, pred_scores)
      eval_spearman, _ = spearmanr(self.scores, pred_scores)
  
      logger.info(
          "Correlation:\tPearson: {:.4f}\tSpearman: {:.4f}".format(
              eval_pearson, eval_spearman
          )
      )
  
      if output_path is not None and self.write_csv:
          csv_path = os.path.join(output_path, self.csv_file)
          output_file_exists = os.path.isfile(csv_path)
          with open(
              csv_path,
              mode="a" if output_file_exists else "w",
              encoding="utf-8",
          ) as f:
              writer = csv.writer(f)
              if not output_file_exists:
                  writer.writerow(self.csv_headers)
  
              writer.writerow(
                  [epoch, steps, eval_pearson, eval_spearman]
              )
  
      return eval_spearman
  
file_path: sentence-transformers/sentence_transformers/cross_encoder/evaluation/CECorrelationEvaluator.py
imports: import logging
  from scipy.stats import pearsonr, spearmanr
  from typing import List
  import os
  import csv
  from ...None import InputExample
label_desc: Use image node_id 3 for calling the CECorrelationEvaluator obj's underlying member method code with example usage: obj.__call__(model, output_path, epoch, steps) and returns: eval_spearman