class_docstr: SNIPS dataset class
  
  Args:
          path (str): dataset path
          sentence_length (int, optional): max sentence length
          word_length (int, optional): max word length
class_docstr_tok: ["SNIPS","dataset","class","Args",":","path","(","str",")",":","dataset","path","sentence_length","(","int",",","optional",")",":","max","sentence","length","word_length","(","int",",","optional",")",":","max","word","length"]
code: def _parse_json(self, data):
      tok = SpacyInstance(
          disable=["tagger", "ner", "parser", "vectors", "textcat"]
      )
      sentences = []
      for s in data:
          tokens = []
          tags = []
          for t in s:
              new_tokens = tok.tokenize(t["text"].strip())
              tokens += new_tokens
              ent = t.get("entity", None)
              if ent is not None:
                  tags += self._create_tags(ent, len(new_tokens))
              else:
                  tags += ["O"] * len(new_tokens)
          sentences.append((tokens, tags))
      return sentences
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import json
  import os
  import sys
  import numpy
  from nlp_architect.utils.generic import pad_sentences
  from nlp_architect.utils.text import SpacyInstance, Vocabulary, character_vector_generator, word_vector_generator
label_desc: Use image node_id 4 for calling the SNIPS obj's underlying member method code with example usage: obj._parse_json(data) and returns: sentences