Examples¶
Note: We are working on new building blocks and datasets. Some of the components in the examples (e.g. Field) will eventually retire. See the release note 0.5.0 here.
- Ability to describe declaratively how to load a custom NLP dataset that’s in a “normal” format:
pos = data.TabularDataset(
path='data/pos/pos_wsj_train.tsv', format='tsv',
fields=[('text', data.Field()),
('labels', data.Field())])
sentiment = data.TabularDataset(
path='data/sentiment/train.json', format='json',
fields={'sentence_tokenized': ('text', data.Field(sequential=True)),
'sentiment_gold': ('labels', data.Field(sequential=False))})
- Ability to parse nested keys for loading a JSON dataset
2.1 sample.json
{"foods": {
"fruits": ["Apple", "Banana"],
"vegetables": [{"name": "lettuce"}, {"name": "marrow"}]
}
}
2.2 pass in nested keys to parse nested data directly
In [1]: from torchtext import data
In [2]: fields = {'foods.vegetables.name': ('vegs', data.Field())}
In [3]: dataset = data.TabularDataset(path='sample.json', format='json', fields=fields)
In [4]: dataset.examples[0].vegs
Out[4]: ['lettuce', 'marrow']
- Ability to define a preprocessing pipeline:
src = data.Field(tokenize=my_custom_tokenizer)
trg = data.Field(tokenize=my_custom_tokenizer)
mt_train = datasets.TranslationDataset(
path='data/mt/wmt16-ende.train', exts=('.en', '.de'),
fields=(src, trg))
- Batching, padding, and numericalizing (including building vocabulary object):
# continuing from above
mt_dev = data.TranslationDataset(
path='data/mt/newstest2014', exts=('.en', '.de'),
fields=(src, trg))
src.build_vocab(mt_train, max_size=80000)
trg.build_vocab(mt_train, max_size=40000)
# mt_dev shares the fields, so it shares their vocab objects
train_iter = data.BucketIterator(
dataset=mt_train, batch_size=32,
sort_key=lambda x: data.interleave_keys(len(x.src), len(x.trg)))
# usage
>>>next(iter(train_iter))
<data.Batch(batch_size=32, src=[LongTensor (32, 25)], trg=[LongTensor (32, 28)])>
- Wrapper for dataset splits (train, validation, test):
TEXT = data.Field()
LABELS = data.Field()
train, val, test = data.TabularDataset.splits(
path='/data/pos_wsj/pos_wsj', train='_train.tsv',
validation='_dev.tsv', test='_test.tsv', format='tsv',
fields=[('text', TEXT), ('labels', LABELS)])
train_iter, val_iter, test_iter = data.BucketIterator.splits(
(train, val, test), batch_sizes=(16, 256, 256),
sort_key=lambda x: len(x.text), device=0)
TEXT.build_vocab(train)
LABELS.build_vocab(train)