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 __init__(
      self,
      word_file_path=None,
      save_path=None,
      load_path=None,
      char_level=False,
      **kwargs,
  ):
      super().__init__(
          save_path=save_path, load_path=load_path, **kwargs
      )
  
      self.word_file_path = word_file_path
      self.char_level = char_level
  
      if word_file_path is not None:
          self.load_from_file(word_file_path)
          if self.save_path is not None:
              self.save_to_file(self.save_path)
      elif self.load_path is not None:
          self.load_from_file(self.load_path)
  
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 1 to create a new NerVocab object from inherited base classes: Estimator with example: obj = NerVocab(word_file_path, save_path, load_path, char_level)