docstr: read file
  return format :
  [ ['EU', 'B-ORG'], ['rejects', 'O'], ['German', 'B-MISC'], ['call', 'O'], ['to', 'O'], ['boycott', 'O'], 
  ['British', 'B-MISC'], ['lamb', 'O'], ['.', 'O'] ]
docstr_tok: ["read","file","return","format",":","[","[","'EU","'",",","'","B-ORG","'","]",",","[","'rejects","'",",","'","O","'","]",",","[","'German","'",",","'","B-MISC","'","]",",","[","'call","'",",","'","O","'","]",",","[","'to","'",",","'","O","'","]",",","[","'boycott","'",",","'","O","'","]",",","[","'British","'",",","'","B-MISC","'","]",",","[","'lamb","'",",","'","O","'","]",",","[","'",".","'",",","'","O","'","]","]"]
code: def read_col_file(self, filename):
      """
      read file
      return format :
      [ ['EU', 'B-ORG'], ['rejects', 'O'], ['German', 'B-MISC'], ['call', 'O'], ['to', 'O'], ['boycott', 'O'],
      ['British', 'B-MISC'], ['lamb', 'O'], ['.', 'O'] ]
      """
      f = open(filename)
      data = []
      sentence = []
      label = []
      for line in f:
          if (
              len(line) == 0
              or line.startswith("-DOCSTART")
              or line[0] == "\n"
          ):
              if len(sentence) > 0:
                  data.append((sentence, label))
                  sentence = []
                  label = []
              continue
          splits = line.split(" ")
          sentence.append(splits[0])
          label.append(splits[-1][:-1])
  
      if len(sentence) > 0:
          data.append((sentence, label))
          sentence = []
          label = []
      return data
  
imports: import pandas
  import os
  import torch
  from pathlib import Path
  import pickle
  from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer
label_desc: Use image node_id 7 for calling the NERTextProcessor obj's underlying member method code with example usage: obj.read_col_file(filename) and returns: data