code: def evaluate():
      corpus = join(
          DATASETS_FOLDER, "UD_Vietnamese-UUD-1.0.1-alpha", "all.txt"
      )
      predict_file = join(
          DATASETS_FOLDER,
          "UD_Vietnamese-UUD-1.0.1-alpha",
          "predict.txt",
      )
      with open(corpus) as f:
          text_true = f.read()
      with open(predict_file) as f:
          text_predict = f.read()
      sentences_true = text_true.split("\n\n")
      sentences_predict = text_predict.split("\n\n")
      total = 0.0
      correct = 0.0
      for i in range(len(sentences_true)):
          rows_true = sentences_true[i].split("\n")
          rows_predict = sentences_predict[i].split("\n")
          for j in range(len(rows_true)):
              if rows_true[j].startswith("#"):
                  continue
              lemma_true = rows_true[j].split("\t")[1]
              lemma_predict = rows_predict[j].split("\t")[1]
              total += 1.0
              if lemma_true == lemma_predict:
                  correct += 1.0
      accuracy = correct / total
      print("Evaluation done")
      print(f"Accuracy: {accuracy}")
  
file_path: underthesea/examples/text_normalize/evaluation.py
imports: from os.path import join
  from tools import vtt
  from tools import nvh
  from tools import vtm
  import hydra
  from omegaconf import DictConfig, OmegaConf
  from underthesea import text_normalize
  from underthesea.file_utils import DATASETS_FOLDER
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate() without return types