Data loaders and abstractions for text and NLP
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README.rst

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torchtext

This repository consists of:

  • torchtext.data: Generic data loaders, abstractions, and iterators for text (including vocabulary and word vectors)
  • torchtext.datasets: Pre-built loaders for common NLP datasets

Installation

Make sure you have Python 2.7 or 3.5+ and PyTorch 0.4.0 or newer. You can then install torchtext using pip:

pip install torchtext

For PyTorch versions before 0.4.0, please use pip install torchtext==0.2.3.

Optional requirements

If you want to use English tokenizer from SpaCy, you need to install SpaCy and download its English model:

pip install spacy
python -m spacy download en

Alternatively, you might want to use Moses tokenizer from NLTK. You have to install NLTK and download the data needed:

pip install nltk
python -m nltk.downloader perluniprops nonbreaking_prefixes

Documentation

Find the documentation here.

Data

The data module provides the following:

  • 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 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 a vocabulary object):

    >>> # continuing from above
    >>> mt_dev = datasets.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)

Datasets

The datasets module currently contains:

  • Sentiment analysis: SST and IMDb
  • Question classification: TREC
  • Entailment: SNLI, MultiNLI
  • Language modeling: abstract class + WikiText-2, WikiText103, PennTreebank
  • Machine translation: abstract class + Multi30k, IWSLT, WMT14
  • Sequence tagging (e.g. POS/NER): abstract class + UDPOS, CoNLL2000Chunking
  • Question answering: 20 QA bAbI tasks

Others are planned or a work in progress:

  • Question answering: SQuAD

See the test directory for examples of dataset usage.