class_docstr: This evaluator can be used with the CrossEncoder class. Given sentence pairs and binary labels (0 and 1),
  it compute the average precision and the best possible f1 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(
          "CEBinaryClassificationEvaluator: Evaluating the model on "
          + self.name
          + " dataset"
          + out_txt
      )
      pred_scores = model.predict(
          self.sentence_pairs,
          convert_to_numpy=True,
          show_progress_bar=self.show_progress_bar,
      )
  
      (
          acc,
          acc_threshold,
      ) = BinaryClassificationEvaluator.find_best_acc_and_threshold(
          pred_scores, self.labels, True
      )
      (
          f1,
          precision,
          recall,
          f1_threshold,
      ) = BinaryClassificationEvaluator.find_best_f1_and_threshold(
          pred_scores, self.labels, True
      )
      ap = average_precision_score(self.labels, pred_scores)
  
      logger.info(
          "Accuracy:           {:.2f}\t(Threshold: {:.4f})".format(
              acc * 100, acc_threshold
          )
      )
      logger.info(
          "F1:                 {:.2f}\t(Threshold: {:.4f})".format(
              f1 * 100, f1_threshold
          )
      )
      logger.info("Precision:          {:.2f}".format(precision * 100))
      logger.info("Recall:             {:.2f}".format(recall * 100))
      logger.info("Average Precision:  {:.2f}\n".format(ap * 100))
  
      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,
                      acc,
                      acc_threshold,
                      f1,
                      f1_threshold,
                      precision,
                      recall,
                      ap,
                  ]
              )
  
      return ap
  
file_path: sentence-transformers/sentence_transformers/cross_encoder/evaluation/CEBinaryClassificationEvaluator.py
imports: import logging
  from sklearn.metrics import average_precision_score
  from typing import List
  import numpy
  import os
  import csv
  from ...None import InputExample
  from ...evaluation import BinaryClassificationEvaluator
label_desc: Use image node_id 3 for calling the CEBinaryClassificationEvaluator obj's underlying member method code with example usage: obj.__call__(model, output_path, epoch, steps) and returns: ap