code: def get_classification_result(hyp_file, ref_file):
      hyp_lines = [line for line in hyp_file]
      ref_lines = [line for line in ref_file]
  
      error = 0
      hyp_intent_arr = []
      ref_intent_arr = []
      for line_count in range(len(hyp_lines)):
          hyp_intent = (
              hyp_lines[line_count].split("\t")[0].split(" ")[0]
          )
          ref_intent = (
              ref_lines[line_count].split("\t")[0].split(" ")[0]
          )
          hyp_intent_arr.append(hyp_intent)
          ref_intent_arr.append(ref_intent)
      print(
          classification_report(
              ref_intent_arr, hyp_intent_arr, digits=3
          )
      )
      print(
          classification_report(
              ref_intent_arr,
              hyp_intent_arr,
              digits=3,
              labels=["Neutral", "Positive"],
          )
      )
      return f1_score(ref_intent_arr, hyp_intent_arr, average="macro")
  
imports: import argparse
  import os
  import re
  import sys
  import pandas
  from sklearn.metrics import classification_report, f1_score
label_desc: Use image node_id 1 for calling a global function with example usage: get_classification_result(hyp_file, ref_file) and returns: f1_score