new_contents: """
  Tests the correct computation of evaluation scores from BinaryClassificationEvaluator
  """
  from sentence_transformers import SentenceTransformer, evaluation
  import unittest
  from sklearn.metrics import f1_score, accuracy_score
  import numpy as np
  
  class BinaryClassificationEvaluatorTest(unittest.TestCase):
  
      def test_find_best_f1_and_threshold(self):
          """Tests that the F1 score for the computed threshold is correct"""
          y_true = np.random.randint(0, 2, 1000)
          y_pred_cosine = np.random.randn(1000)
          best_f1, best_precision, best_recall, threshold = evaluation.BinaryClassificationEvaluator.find_best_f1_and_threshold(y_pred_cosine, y_true, high_score_more_similar=True)
          y_pred_labels = [1 if pred >= threshold else 0 for pred in y_pred_cosine]
          sklearn_f1score = f1_score(y_true, y_pred_labels)
          assert np.abs(best_f1 - sklearn_f1score) < 1e-6
  
  
      def test_find_best_accuracy_and_threshold(self):
          """Tests that the Acc score for the computed threshold is correct"""
          y_true = np.random.randint(0, 2, 1000)
          y_pred_cosine = np.random.randn(1000)
          max_acc, threshold = evaluation.BinaryClassificationEvaluator.find_best_acc_and_threshold(y_pred_cosine, y_true, high_score_more_similar=True)
          y_pred_labels = [1 if pred >= threshold else 0 for pred in y_pred_cosine]
          sklearn_acc = accuracy_score(y_true, y_pred_labels)
          assert np.abs(max_acc - sklearn_acc) < 1e-6
  
subject: Add test case