class_docstr: This evaluator can be used with the CrossEncoder class.
  
  It is designed for CrossEncoders with 1 outputs. It measure the
  accuracy of the predict class vs. the gold labels. It uses a fixed threshold to determine the label (0 vs 1).
  
  See CEBinaryClassificationEvaluator for an evaluator that determines automatically the optimal threshold.
code: def __init__(
      self,
      sentence_pairs: List[List[str]],
      labels: List[int],
      name: str = "",
      threshold: float = 0.5,
      write_csv: bool = True,
  ):
      self.sentence_pairs = sentence_pairs
      self.labels = labels
      self.name = name
      self.threshold = threshold
  
      self.csv_file = (
          "CEBinaryAccuracyEvaluator"
          + ("_" + name if name else "")
          + "_results.csv"
      )
      self.csv_headers = ["epoch", "steps", "Accuracy"]
      self.write_csv = write_csv
  
file_path: sentence-transformers/sentence_transformers/cross_encoder/evaluation/CEBinaryAccuracyEvaluator.py
imports: import logging
  import os
  import csv
  from typing import List
  from ...None import InputExample
  import numpy
label_desc: Use image node_id 1 to create a new CEBinaryAccuracyEvaluator object  with example: obj = CEBinaryAccuracyEvaluator(sentence_pairs, labels, name, threshold, write_csv)