Text utilities and datasets for PyTorch
Project description
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.
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