Torchtext snippets

bebound@gmail.com (KK) — Mon, 01 Jul 2019 21:28:00 +0800

Load separate files

data.Field parameters is here.

When calling build_vocab, torchtext will add <unk> in vocabulary list. Set unk_token=None if you want to remove it. If sequential=True (default), it will add <pad> in vocab. <unk> and <pad> will add at the beginning of vocabulary list by default.

LabelField is similar to Field, but it will set sequential=False, unk_token=None and is_target=Ture

INPUT = data.Field(lower=True, batch_first=True)
TAG = data.LabelField()

train, val, test = data.TabularDataset.splits(path=base_dir.as_posix(), train='train_data.csv',
 validation='val_data.csv', test='test_data.csv',
 format='tsv',
 fields=[(None, None), ('input', INPUT), ('tag', TAG)])

Load single file

all_data = data.TabularDataset(path=base_dir / 'gossip_train_data.csv',
 format='tsv',
 fields=[('text', TEXT), ('category', CATEGORY)])
train, val, test = all_data.split([0.7, 0.2, 0.1])

Create iterator

train_iter, val_iter, test_iter = data.BucketIterator.splits(
 (train, val, test), batch_sizes=(32, 256, 256), shuffle=True,
 sort_key=lambda x: x.input)

Load pretrained vector

vectors = Vectors(name='cc.zh.300.vec', cache='./')

INPUT.build_vocab(train, vectors=vectors)
TAG.build_vocab(train, val, test)

Check vocab sizes