class_docstr: Implementation of the NER vocabulary
  
  Params:
      word_file_path: the path to the pre-trained word embedding model
      save_path: the folder path to save dictionary files
      load_path: the folder path from which the dictionary files are loaded
      char_level: the flag arg indicating the character vocabulary
class_docstr_tok: ["Implementation","of","the","NER","vocabulary","Params",":","word_file_path",":","the","path","to","the","pre-trained","word","embedding","model","save_path",":","the","folder","path","to","save","dictionary","files","load_path",":","the","folder","path","from","which","the","dictionary","files","are","loaded","char_level",":","the","flag","arg","indicating","the","character","vocabulary"]
code: def load_from_file(self, filename):
      if filename is None or not os.path.exists(filename):
          return
  
      self._t2i, self._i2t = {}, {}
      for i, line in enumerate(
          open(file=filename, mode="r", encoding="utf-8").readlines()
      ):
          word = line.strip()
          self._t2i[word] = i
          self._i2t[i] = word
  
file_path: DeepPavlov/deeppavlov/models/preprocessors/ner_preprocessor.py
imports: import errno
  import os
  from logging import getLogger
  from typing import List
  import numpy
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.estimator import Estimator
label_desc: Use image node_id 2 for calling the NerVocab obj's underlying member method code with example usage: obj.load_from_file(filename) without return types