class_docstr: Reads a file from the Quora Paraphrase dataset. The train/validation/test split of the data
  comes from the paper `Bilateral Multi-Perspective Matching for Natural Language Sentences
  <https://arxiv.org/abs/1702.03814>`_ by Zhiguo Wang et al., 2017. Each file of the data
  is a tsv file without header. The columns are is_duplicate, question1, question2, and id.
  All questions are pre-tokenized and tokens are space separated. We convert these keys into
  fields named "label", "premise" and "hypothesis", so that it is compatible to some existing
  natural language inference algorithms.
  
  Parameters
  ----------
  lazy : ``bool`` (optional, default=False)
      Passed to ``DatasetReader``.  If this is ``True``, training will start sooner, but will
      take longer per batch.  This also allows training with datasets that are too large to fit
      in memory.
  tokenizer : ``Tokenizer``, optional
      Tokenizer to use to split the premise and hypothesis into words or other kinds of tokens.
      Defaults to ``WordTokenizer(JustSpacesWordSplitter())``.
  token_indexers : ``Dict[str, TokenIndexer]``, optional
      Indexers used to define input token representations. Defaults to ``{"tokens":
      SingleIdTokenIndexer()}``.
class_docstr_tok: ["Reads","a","file","from","the","Quora","Paraphrase","dataset",".","The","train\/validation\/test","split","of","the","data","comes","from","the","paper","`","Bilateral","Multi-Perspective","Matching","for","Natural","Language","Sentences","<","https",":","\/\/arxiv.org\/abs\/1702.03814",">","`","_","by","Zhiguo","Wang","et","al.",",","2017",".","Each","file","of","the","data","is","a","tsv","file","without","header",".","The","columns","are","is_duplicate",",","question1",",","question2",",","and","id",".","All","questions","are","pre-tokenized","and","tokens","are","space","separated",".","We","convert","these","keys","into","fields","named","``","label","''",",","``","premise","''","and","``","hypothesis","''",",","so","that","it","is","compatible","to","some","existing","natural","language","inference","algorithms",".","Parameters","--","--","--","--","--","lazy",":","``","bool","``","(","optional",",","default=False",")","Passed","to","``","DatasetReader","``",".","If","this","is","``","True","``",",","training","will","start","sooner",",","but","will","take","longer","per","batch",".","This","also","allows","training","with","datasets","that","are","too","large","to","fit","in","memory",".","tokenizer",":","``","Tokenizer","``",",","optional","Tokenizer","to","use","to","split","the","premise","and","hypothesis","into","words","or","other","kinds","of","tokens",".","Defaults","to","``","WordTokenizer","(","JustSpacesWordSplitter","(",")",")","``",".","token_indexers",":","``","Dict","[","str",",","TokenIndexer","]","``",",","optional","Indexers","used","to","define","input","token","representations",".","Defaults","to","``","{","``","tokens","''",":","SingleIdTokenIndexer","(",")","}","``","."]
code: def __init__(self, lazy=False, tokenizer=None, token_indexers=None):
      super(QuoraParaphraseDatasetReader, self).__init__(lazy)
      self._tokenizer = tokenizer or WordTokenizer(
          JustSpacesWordSplitter()
      )
      self._token_indexers = token_indexers or {
          "tokens": SingleIdTokenIndexer()
      }
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/dataset_readers/quora_paraphrase.py
imports: from __future__ import with_statement
  from __future__ import absolute_import
  import logging
  import csv
  from allennlp.common.file_utils import cached_path
  from allennlp.data.dataset_readers.dataset_reader import DatasetReader
  from allennlp.data.fields import LabelField, TextField, Field
  from allennlp.data.instance import Instance
  from allennlp.data.tokenizers import Tokenizer, WordTokenizer
  from allennlp.data.tokenizers.word_splitter import JustSpacesWordSplitter
  from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer
  from io import open
label_desc: Use image node_id 1 to create a new QuoraParaphraseDatasetReader object from inherited base classes: DatasetReader with example: obj = QuoraParaphraseDatasetReader(lazy, tokenizer, token_indexers)