class_docstr: Computes the mean squared error (x100) between the computed sentence embedding
  and some target sentence embedding.
  
  The MSE is computed between ||teacher.encode(source_sentences) - student.encode(target_sentences)||.
  
  For multilingual knowledge distillation (https://arxiv.org/abs/2004.09813), source_sentences are in English
  and target_sentences are in a different language like German, Chinese, Spanish...
  
  :param source_sentences: Source sentences are embedded with the teacher model
  :param target_sentences: Target sentences are ambedding with the student model.
  :param show_progress_bar: Show progress bar when computing embeddings
  :param batch_size: Batch size to compute sentence embeddings
  :param name: Name of the evaluator
  :param write_csv: Write results to CSV file
code: def __init__(
      self,
      source_sentences: List[str],
      target_sentences: List[str],
      teacher_model=None,
      show_progress_bar: bool = False,
      batch_size: int = 32,
      name: str = "",
      write_csv: bool = True,
  ):
      self.source_embeddings = teacher_model.encode(
          source_sentences,
          show_progress_bar=show_progress_bar,
          batch_size=batch_size,
          convert_to_numpy=True,
      )
  
      self.target_sentences = target_sentences
      self.show_progress_bar = show_progress_bar
      self.batch_size = batch_size
      self.name = name
  
      self.csv_file = "mse_evaluation_" + name + "_results.csv"
      self.csv_headers = ["epoch", "steps", "MSE"]
      self.write_csv = write_csv
  
file_path: sentence-transformers/sentence_transformers/evaluation/MSEEvaluator.py
imports: from sentence_transformers.evaluation import SentenceEvaluator
  import logging
  import os
  import csv
  from typing import List
label_desc: Use image node_id 1 to create a new MSEEvaluator object from inherited base classes: SentenceEvaluator with example: obj = MSEEvaluator(source_sentences, target_sentences, teacher_model, show_progress_bar, batch_size, name, write_csv)