class_docstr: NER preprocessing base class
class_docstr_tok: ["NER","preprocessing","base","class"]
code: def get_wv_model(self, wv_path_or_url, verbose=1):
      if wv_path_or_url is None:
          raise ValueError(
              "wordvector_path_or_url is empty: supply a file path or "
              + "URL to fasttext word vector file"
          )
      if verbose:
          print(
              "pretrained word embeddings will be loaded from:\n\t%s"
              % (wv_path_or_url)
          )
      word_embedding_dim = (
          300  # all fasttext word vectors are of dim=300
      )
      embs = tpp.load_wv(wv_path_or_url, verbose=verbose)
      wv_model = self.filter_embeddings(
          embs, self.p._word_vocab.vocab, word_embedding_dim
      )
      return (wv_model, word_embedding_dim)
  
imports: from ...None import utils
  from ...imports import *
  from ...preprocessor import Preprocessor
  from ..None import preprocessor
  from ..None import textutils
label_desc: Use image node_id 5 for calling the NERPreprocessor obj's underlying member method code with example usage: obj.get_wv_model(wv_path_or_url, verbose) and returns: wv_model,  word_embedding_dim