pytorch / Text

torchtext

This repository consists of:

• torchtext.datasets: The raw text iterators for common NLP datasets
• torchtext.data: Some basic NLP building blocks (tokenizers, metrics, functionals etc.)
• torchtext.nn: NLP related modules
• torchtext.vocab: Vocab and Vectors related classes and factory functions
• examples: Example NLP workflows with PyTorch and torchtext library.

Note: The legacy code discussed in torchtext v0.7.0 release note has been retired to torchtext.legacy folder. Those legacy code will not be maintained by the development team, and we plan to fully remove them in the future release. See torchtext.legacy folder for more details.

Installation

We recommend Anaconda as a Python package management system. Please refer to pytorch.org for the details of PyTorch installation. The following are the corresponding torchtext versions and supported Python versions.

Using conda:

conda install -c pytorch torchtext

Using pip:

pip install torchtext

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_core_web_sm

Alternatively, you might want to use the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses:

pip install sacremoses

For torchtext 0.5 and below, sentencepiece:

conda install -c powerai sentencepiece

Building from source

To build torchtext from source, you need git, CMake and C++11 compiler such as g++.:

git clone https://github.com/pytorch/text torchtext
cd torchtext
git submodule update --init --recursive

# Linux
python setup.py clean install

# OSX
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py clean install

# or ``python setup.py develop`` if you are making modifications.

Note

When building from source, make sure that you have the same C++ compiler as the one used to build PyTorch. A simple way is to build PyTorch from source and use the same environment to build torchtext. If you are using the nightly build of PyTorch, checkout the environment it was built with conda (here) and pip (here).

Documentation

Find the documentation here.

Datasets

The datasets module currently contains:

• Language modeling: WikiText2, WikiText103, PennTreebank, EnWik9
• Machine translation: IWSLT2016, IWSLT2017, Multi30k
• Sequence tagging (e.g. POS/NER): UDPOS, CoNLL2000Chunking
• Question answering: SQuAD1, SQuAD2
• Text classification: AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, YelpReviewFull, YahooAnswers, AmazonReviewPolarity, AmazonReviewFull, IMDB

For example, to access the raw text from the AG_NEWS dataset:

>>> from torchtext.datasets import AG_NEWS
>>> train_iter = AG_NEWS(split='train')
>>> next(train_iter)
>>> # Or iterate with for loop
>>> for (label, line) in train_iter:
>>>     print(label, line)
>>> # Or send to DataLoader
>>> from torch.utils.data import DataLoader
>>> train_iter = AG_NEWS(split='train')
>>> dataloader = DataLoader(train_iter, batch_size=8, shuffle=False)

Tutorials

To get started with torchtext, users may refer to the following tutorials available on PyTorch website.

• Text classification with AG_NEWS dataset
• Translation trained with Multi30k dataset using transformers and torchtext
• Language modeling using transforms and torchtext

[BC Breaking] Legacy

In the v0.9.0 release, we moved the following legacy code to torchtext.legacy. This is part of the work to revamp the torchtext library and the motivation has been discussed in Issue #664:

• torchtext.legacy.data.field
• torchtext.legacy.data.batch
• torchtext.legacy.data.example
• torchtext.legacy.data.iterator
• torchtext.legacy.data.pipeline
• torchtext.legacy.datasets

We have a migration tutorial to help users switch to the torchtext datasets in v0.9.0 release. For the users who still want the legacy components, they can add legacy to the import path.

In the v0.10.0 release, we retire the Vocab class to torchtext.legacy. Users can still access the legacy Vocab via torchtext.legacy.vocab. This class has been replaced by a Vocab module that is backed by efficient C++ implementation and provides common functional APIs for NLP workflows.

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!