text: import numpy as np
  import torch
  from torchtext.data.field import Field, RawField
  from torchtext.data.iterator import BucketIterator
  from torchtext.vocab import Vectors
  
  from datasets.castor_dataset import CastorPairDataset
  
  
  class Quora(CastorPairDataset):
      NAME = 'Quora'
      NUM_CLASSES = 2
      ID_FIELD = Field(sequential=False, tensor_type=torch.FloatTensor, use_vocab=False, batch_first=True)
      AID_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
      TEXT_FIELD = Field(batch_first=True, tokenize=lambda x: x)  # tokenizer is identity since we already tokenized it to compute external features
      EXT_FEATS_FIELD = Field(tensor_type=torch.FloatTensor, use_vocab=False, batch_first=True, tokenize=lambda x: x)
      LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
      RAW_TEXT_FIELD = RawField()
      VOCAB_SIZE = 0
  
  
      @staticmethod
      def sort_key(ex):
          return len(ex.sentence_1)
  
      def __init__(self, path):
          """
          Create a Quora dataset instance
          """
          super(Quora, self).__init__(path)
  
      @classmethod
      def splits(cls, path, train='train', validation='dev', test='test', **kwargs):
          return super(Quora, cls).splits(path, train=train, validation=validation, test=test, **kwargs)
  
      @classmethod
      def iters(cls, path, vectors_name, vectors_dir, batch_size=64, shuffle=True, device=0, pt_file=False, vectors=None,
                unk_init=torch.Tensor.zero_):
          """
          :param path: directory containing train, test, dev files
          :param vectors_name: name of word vectors file
          :param vectors_dir: directory containing word vectors file
          :param batch_size: batch size
          :param device: GPU device
          :param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
          :param pt_file: load cached embedding file from disk if it is true
          :param unk_init: function used to generate vector for OOV words
          :return:
          """
  
          train, validation, test = cls.splits(path)
          if not pt_file:
              if vectors is None:
                  vectors = Vectors(name=vectors_name, cache=vectors_dir, unk_init=unk_init)
              cls.TEXT_FIELD.build_vocab(train, validation, test, vectors=vectors)
          else:
              cls.TEXT_FIELD.build_vocab(train, validation, test)
              cls.TEXT_FIELD = cls.set_vectors(cls.TEXT_FIELD, os.path.join(vectors_dir, vectors_name))
  
          cls.LABEL_FIELD.build_vocab(train, validation, test)
  
          cls.VOCAB_SIZE = len(cls.TEXT_FIELD.vocab)
  
          return BucketIterator.splits((train, validation, test), batch_size=batch_size, repeat=False, shuffle=shuffle,
                                       sort_within_batch=True, device=device)