.. image:: docs/source/_static/img/torchtext_logo.png
.. image:: https://circleci.com/gh/pytorch/text.svg?style=svg
:target: https://circleci.com/gh/pytorch/text
.. image:: https://codecov.io/gh/pytorch/text/branch/main/graph/badge.svg
:target: https://codecov.io/gh/pytorch/text
.. image:: https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchtext%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
:target: https://pytorch.org/text/
torchtext
+++++++++
CAUTION: As of September 2023 we have paused active development of TorchText because our focus has shifted away from building out this library offering.
We will continue to release new versions but do not anticipate any new feature development as we figure out future investments in this space.
This repository consists of:
* `torchtext.datasets <https://github.com/pytorch/text/tree/main/torchtext/datasets>`_: The raw text iterators for common NLP datasets
* `torchtext.data <https://github.com/pytorch/text/tree/main/torchtext/data>`_: Some basic NLP building blocks
* `torchtext.transforms <https://github.com/pytorch/text/tree/main/torchtext/transforms.py>`_: Basic text-processing transformations
* `torchtext.models <https://github.com/pytorch/text/tree/main/torchtext/models>`_: Pre-trained models
* `torchtext.vocab <https://github.com/pytorch/text/tree/main/torchtext/vocab>`_: Vocab and Vectors related classes and factory functions
* `examples <https://github.com/pytorch/text/tree/main/examples>`_: Example NLP workflows with PyTorch and torchtext library.
Installation
============
We recommend Anaconda as a Python package management system. Please refer to `pytorch.org <https://pytorch.org/>`_ for the details of PyTorch installation. The following are the corresponding ``torchtext`` versions and supported Python versions.
.. csv-table:: Version Compatibility
:header: "PyTorch version", "torchtext version", "Supported Python version"
:widths: 10, 10, 10
nightly build, main, ">=3.8, <=3.11"
2.2.0, 0.17.0, ">=3.8, <=3.11"
2.1.0, 0.16.0, ">=3.8, <=3.11"
2.0.0, 0.15.0, ">=3.8, <=3.11"
1.13.0, 0.14.0, ">=3.7, <=3.10"
1.12.0, 0.13.0, ">=3.7, <=3.10"
1.11.0, 0.12.0, ">=3.6, <=3.9"
1.10.0, 0.11.0, ">=3.6, <=3.9"
1.9.1, 0.10.1, ">=3.6, <=3.9"
1.9, 0.10, ">=3.6, <=3.9"
1.8.1, 0.9.1, ">=3.6, <=3.9"
1.8, 0.9, ">=3.6, <=3.9"
1.7.1, 0.8.1, ">=3.6, <=3.9"
1.7, 0.8, ">=3.6, <=3.8"
1.6, 0.7, ">=3.6, <=3.8"
1.5, 0.6, ">=3.5, <=3.8"
1.4, 0.5, "2.7, >=3.5, <=3.8"
0.4 and below, 0.2.3, "2.7, >=3.5, <=3.8"
Using conda::
conda install -c pytorch torchtext
Using pip::
pip install torchtext
Optional requirements
---------------------
If you want to use English tokenizer from `SpaCy <http://spacy.io/>`_, 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 <http://www.statmt.org/moses/>`_ tokenizer port in `SacreMoses <https://github.com/alvations/sacremoses>`_ (split from `NLTK <http://nltk.org/>`_). 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
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) <https://github.com/pytorch/builder/tree/main/conda>`_ and `pip (here) <https://github.com/pytorch/builder/tree/main/manywheel>`_.
Additionally, datasets in torchtext are implemented using the torchdata library. Please take a look at the
`installation instructions <https://github.com/pytorch/data#installation>`_ to download the latest nightlies or install from source.
Documentation
=============
Find the documentation `here <https://pytorch.org/text/>`_.
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: SST2, AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, YelpReviewFull, YahooAnswers, AmazonReviewPolarity, AmazonReviewFull, IMDB
* Model pre-training: CC-100
Models
======
The library currently consist of following pre-trained models:
* RoBERTa: `Base and Large Architecture <https://github.com/pytorch/fairseq/tree/main/examples/roberta#pre-trained-models>`_
* `DistilRoBERTa <https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md>`_
* XLM-RoBERTa: `Base and Large Architure <https://github.com/pytorch/fairseq/tree/main/examples/xlmr#pre-trained-models>`_
* T5: `Small, Base, Large, 3B, and 11B Architecture <https://github.com/google-research/text-to-text-transfer-transformer>`_
* Flan-T5: `Base, Large, XL, and XXL Architecture <https://github.com/google-research/t5x>`_
Tokenizers
==========
The transforms module currently support following scriptable tokenizers:
* `SentencePiece <https://github.com/google/sentencepiece>`_
* `GPT-2 BPE <https://github.com/openai/gpt-2/blob/master/src/encoder.py>`_
* `CLIP <https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py>`_
* `RE2 <https://github.com/google/re2>`_
* `BERT <https://arxiv.org/pdf/1810.04805.pdf>`_
Tutorials
=========
To get started with torchtext, users may refer to the following tutorial available on PyTorch website.
* `SST-2 binary text classification using XLM-R pre-trained model <https://pytorch.org/text/stable/tutorials/sst2_classification_non_distributed.html>`_
* `Text classification with AG_NEWS dataset <https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html>`_
* `Translation trained with Multi30k dataset using transformers and torchtext <https://pytorch.org/tutorials/beginner/translation_transformer.html>`_
* `Language modeling using transforms and torchtext <https://pytorch.org/tutorials/beginner/transformer_tutorial.html>`_
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!
Raw data
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"description": ".. image:: docs/source/_static/img/torchtext_logo.png\n\n.. image:: https://circleci.com/gh/pytorch/text.svg?style=svg\n :target: https://circleci.com/gh/pytorch/text\n\n.. image:: https://codecov.io/gh/pytorch/text/branch/main/graph/badge.svg\n :target: https://codecov.io/gh/pytorch/text\n\n.. image:: https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchtext%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v\n :target: https://pytorch.org/text/\n\ntorchtext\n+++++++++\n\nCAUTION: As of September 2023 we have paused active development of TorchText because our focus has shifted away from building out this library offering.\nWe will continue to release new versions but do not anticipate any new feature development as we figure out future investments in this space.\n\nThis repository consists of:\n\n* `torchtext.datasets <https://github.com/pytorch/text/tree/main/torchtext/datasets>`_: The raw text iterators for common NLP datasets\n* `torchtext.data <https://github.com/pytorch/text/tree/main/torchtext/data>`_: Some basic NLP building blocks\n* `torchtext.transforms <https://github.com/pytorch/text/tree/main/torchtext/transforms.py>`_: Basic text-processing transformations\n* `torchtext.models <https://github.com/pytorch/text/tree/main/torchtext/models>`_: Pre-trained models\n* `torchtext.vocab <https://github.com/pytorch/text/tree/main/torchtext/vocab>`_: Vocab and Vectors related classes and factory functions\n* `examples <https://github.com/pytorch/text/tree/main/examples>`_: Example NLP workflows with PyTorch and torchtext library.\n\n\nInstallation\n============\n\nWe recommend Anaconda as a Python package management system. Please refer to `pytorch.org <https://pytorch.org/>`_ for the details of PyTorch installation. The following are the corresponding ``torchtext`` versions and supported Python versions.\n\n.. csv-table:: Version Compatibility\n :header: \"PyTorch version\", \"torchtext version\", \"Supported Python version\"\n :widths: 10, 10, 10\n\n nightly build, main, \">=3.8, <=3.11\"\n 2.2.0, 0.17.0, \">=3.8, <=3.11\"\n 2.1.0, 0.16.0, \">=3.8, <=3.11\"\n 2.0.0, 0.15.0, \">=3.8, <=3.11\"\n 1.13.0, 0.14.0, \">=3.7, <=3.10\"\n 1.12.0, 0.13.0, \">=3.7, <=3.10\"\n 1.11.0, 0.12.0, \">=3.6, <=3.9\"\n 1.10.0, 0.11.0, \">=3.6, <=3.9\"\n 1.9.1, 0.10.1, \">=3.6, <=3.9\"\n 1.9, 0.10, \">=3.6, <=3.9\"\n 1.8.1, 0.9.1, \">=3.6, <=3.9\"\n 1.8, 0.9, \">=3.6, <=3.9\"\n 1.7.1, 0.8.1, \">=3.6, <=3.9\"\n 1.7, 0.8, \">=3.6, <=3.8\"\n 1.6, 0.7, \">=3.6, <=3.8\"\n 1.5, 0.6, \">=3.5, <=3.8\"\n 1.4, 0.5, \"2.7, >=3.5, <=3.8\"\n 0.4 and below, 0.2.3, \"2.7, >=3.5, <=3.8\"\n\nUsing conda::\n\n conda install -c pytorch torchtext\n\nUsing pip::\n\n pip install torchtext\n\nOptional requirements\n---------------------\n\nIf you want to use English tokenizer from `SpaCy <http://spacy.io/>`_, you need to install SpaCy and download its English model::\n\n pip install spacy\n python -m spacy download en_core_web_sm\n\nAlternatively, you might want to use the `Moses <http://www.statmt.org/moses/>`_ tokenizer port in `SacreMoses <https://github.com/alvations/sacremoses>`_ (split from `NLTK <http://nltk.org/>`_). You have to install SacreMoses::\n\n pip install sacremoses\n\nFor torchtext 0.5 and below, ``sentencepiece``::\n\n conda install -c powerai sentencepiece\n\nBuilding from source\n--------------------\n\nTo build torchtext from source, you need ``git``, ``CMake`` and C++11 compiler such as ``g++``.::\n\n git clone https://github.com/pytorch/text torchtext\n cd torchtext\n git submodule update --init --recursive\n\n # Linux\n python setup.py clean install\n\n # OSX\n CC=clang CXX=clang++ python setup.py clean install\n\n # or ``python setup.py develop`` if you are making modifications.\n\n**Note**\n\nWhen 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.\nIf you are using the nightly build of PyTorch, checkout the environment it was built with `conda (here) <https://github.com/pytorch/builder/tree/main/conda>`_ and `pip (here) <https://github.com/pytorch/builder/tree/main/manywheel>`_.\n\nAdditionally, datasets in torchtext are implemented using the torchdata library. Please take a look at the\n`installation instructions <https://github.com/pytorch/data#installation>`_ to download the latest nightlies or install from source.\n\nDocumentation\n=============\n\nFind the documentation `here <https://pytorch.org/text/>`_.\n\nDatasets\n========\n\nThe datasets module currently contains:\n\n* Language modeling: WikiText2, WikiText103, PennTreebank, EnWik9\n* Machine translation: IWSLT2016, IWSLT2017, Multi30k\n* Sequence tagging (e.g. POS/NER): UDPOS, CoNLL2000Chunking\n* Question answering: SQuAD1, SQuAD2\n* Text classification: SST2, AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, YelpReviewFull, YahooAnswers, AmazonReviewPolarity, AmazonReviewFull, IMDB\n* Model pre-training: CC-100\n\nModels\n======\n\nThe library currently consist of following pre-trained models:\n\n* RoBERTa: `Base and Large Architecture <https://github.com/pytorch/fairseq/tree/main/examples/roberta#pre-trained-models>`_\n* `DistilRoBERTa <https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md>`_\n* XLM-RoBERTa: `Base and Large Architure <https://github.com/pytorch/fairseq/tree/main/examples/xlmr#pre-trained-models>`_\n* T5: `Small, Base, Large, 3B, and 11B Architecture <https://github.com/google-research/text-to-text-transfer-transformer>`_\n* Flan-T5: `Base, Large, XL, and XXL Architecture <https://github.com/google-research/t5x>`_\n\nTokenizers\n==========\n\nThe transforms module currently support following scriptable tokenizers:\n\n* `SentencePiece <https://github.com/google/sentencepiece>`_\n* `GPT-2 BPE <https://github.com/openai/gpt-2/blob/master/src/encoder.py>`_\n* `CLIP <https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py>`_\n* `RE2 <https://github.com/google/re2>`_\n* `BERT <https://arxiv.org/pdf/1810.04805.pdf>`_\n\nTutorials\n=========\n\nTo get started with torchtext, users may refer to the following tutorial available on PyTorch website.\n\n* `SST-2 binary text classification using XLM-R pre-trained model <https://pytorch.org/text/stable/tutorials/sst2_classification_non_distributed.html>`_\n* `Text classification with AG_NEWS dataset <https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html>`_\n* `Translation trained with Multi30k dataset using transformers and torchtext <https://pytorch.org/tutorials/beginner/translation_transformer.html>`_\n* `Language modeling using transforms and torchtext <https://pytorch.org/tutorials/beginner/transformer_tutorial.html>`_\n\n\nDisclaimer on Datasets\n======================\n\nThis is a utility library that downloads and prepares public datasets. 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