old_contents: from torchtext import data
  from torchtext import datasets
  from torchtext.vocab import Vectors, GloVe, CharNGram, FastText
  
  
  # Approach 1:
  # set up fields
  TEXT = data.Field()
  LABEL = data.Field(sequential=False)
  
  # make splits for data
  train, val, test = datasets.SST.splits(
      TEXT, LABEL, fine_grained=True, train_subtrees=True,
      filter_pred=lambda ex: ex.label != 'neutral')
  
  # print information about the data
  print('train.fields', train.fields)
  print('len(train)', len(train))
  print('vars(train[0])', vars(train[0]))
  
  # build the vocabulary
  url = 'https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.simple.vec'
  TEXT.build_vocab(train, vectors=Vectors('wiki.simple.vec', url=url))
  LABEL.build_vocab(train)
  
  # print vocab information
  print('len(TEXT.vocab)', len(TEXT.vocab))
  print('TEXT.vocab.vectors.size()', TEXT.vocab.vectors.size())
  
  # make iterator for splits
  train_iter, val_iter, test_iter = data.BucketIterator.splits(
      (train, val, test), batch_size=3)
  
  # print batch information
  batch = next(iter(train_iter))
  print(batch.text)
  print(batch.label)
  
  # Approach 2:
  TEXT.build_vocab(train, vectors=[GloVe(name='840B', dim='300'), CharNGram(), FastText()])
  LABEL.build_vocab(train)
  
  # print vocab information
  print('len(TEXT.vocab)', len(TEXT.vocab))
  print('TEXT.vocab.vectors.size()', TEXT.vocab.vectors.size())
  
  train_iter, val_iter, test_iter = datasets.SST.iters(batch_size=4)
  
  # print batch information
  batch = next(iter(train_iter))
  print(batch.text)
  print(batch.label)
  
  # Approach 3:
  f = FastText()
  TEXT.build_vocab(train, vectors=f)
  TEXT.vocab.extend(f)
  LABEL.build_vocab(train)
  
  # print vocab information
  print('len(TEXT.vocab)', len(TEXT.vocab))
  print('TEXT.vocab.vectors.size()', TEXT.vocab.vectors.size())
  
  train_iter, val_iter, test_iter = datasets.SST.iters(batch_size=4)
  
  # print batch information
  batch = next(iter(train_iter))
  print(batch.text)
  print(batch.label)
  
new_contents: from torchtext import data
  from torchtext import datasets
  from torchtext.vocab import Vectors, GloVe, CharNGram, FastText
  
  
  # Approach 1:
  # set up fields
  TEXT = data.Field()
  LABEL = data.Field(sequential=False)
  
  # make splits for data
  train, val, test = datasets.SST.splits(
      TEXT, LABEL, fine_grained=True, train_subtrees=True,
      filter_pred=lambda ex: ex.label != 'neutral')
  
  # print information about the data
  print('train.fields', train.fields)
  print('len(train)', len(train))
  print('vars(train[0])', vars(train[0]))
  
  # build the vocabulary
  url = 'https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.simple.vec'
  TEXT.build_vocab(train, vectors=Vectors('wiki.simple.vec', url=url))
  LABEL.build_vocab(train)
  
  # print vocab information
  print('len(TEXT.vocab)', len(TEXT.vocab))
  print('TEXT.vocab.vectors.size()', TEXT.vocab.vectors.size())
  
  # make iterator for splits
  train_iter, val_iter, test_iter = data.BucketIterator.splits(
      (train, val, test), batch_size=3)
  
  # print batch information
  batch = next(iter(train_iter))
  print(batch.text)
  print(batch.label)
  
  # Approach 2:
  TEXT.build_vocab(train, vectors=[GloVe(name='840B', dim='300'), CharNGram(), FastText()])
  LABEL.build_vocab(train)
  
  # print vocab information
  print('len(TEXT.vocab)', len(TEXT.vocab))
  print('TEXT.vocab.vectors.size()', TEXT.vocab.vectors.size())
  
  train_iter, val_iter, test_iter = datasets.SST.iters(batch_size=4)
  
  # print batch information
  batch = next(iter(train_iter))
  print(batch.text)
  print(batch.label)
  
  # Approach 3:
  f = FastText()
  TEXT.build_vocab(train, vectors=f)
  TEXT.vocab.extend(f)
  LABEL.build_vocab(train)
  
  # print vocab information
  print('len(TEXT.vocab)', len(TEXT.vocab))
  print('TEXT.vocab.vectors.size()', TEXT.vocab.vectors.size())
  
  train_iter, val_iter, test_iter = datasets.SST.iters(batch_size=4)
  
  # print batch information
  batch = next(iter(train_iter))
  print(batch.text)
  print(batch.label)
  
subject: fix dead url for wiki.simple.vec in tests (#525)