text: A Python repository from ohke

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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

Note: we are currently re-designing the torchtext library to make it more compatible with pytorch (e.g. torch.utils.data). Several datasets have been written with the new abstractions in torchtext.experimental folder. We also created an issue to discuss the new abstraction, and users are welcome to leave feedback here.

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 the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses:

pip install sacremoses

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
Text classification: AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, YelpReviewFull, YahooAnswers, AmazonReviewPolarity, AmazonReviewFull

Others are planned or a work in progress:

Question answering: SQuAD

See the test directory for examples of dataset usage.

Experimental Code

We have re-written several datasets under torchtext.experimental.datasets:

Sentiment analysis: IMDb
Language modeling: abstract class + WikiText-2, WikiText103, PennTreebank

A new pattern is introduced in Release v0.5.0. Several other datasets are also in the new pattern:

Unsupervised learning dataset: Enwik9
Text classification: AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, YelpReviewFull, YahooAnswers, AmazonReviewPolarity, AmazonReviewFull

Disclaimer on Datasets

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

ohke/text