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 __init__(
      self,
      sentence_pairs: List[List[str]],
      scores: List[float],
      name: str = "",
      write_csv: bool = True,
  ):
      self.sentence_pairs = sentence_pairs
      self.scores = scores
      self.name = name
  
      self.csv_file = (
          "CECorrelationEvaluator"
          + ("_" + name if name else "")
          + "_results.csv"
      )
      self.csv_headers = [
          "epoch",
          "steps",
          "Pearson_Correlation",
          "Spearman_Correlation",
      ]
      self.write_csv = write_csv
  
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 1 to create a new CECorrelationEvaluator object  with example: obj = CECorrelationEvaluator(sentence_pairs, scores, name, write_csv)