torchtext

Text utilities, models, transforms, and datasets for PyTorch.

These details have been verified by PyPI

Maintainers

atalman malfet seemethere soumith

These details have not been verified by PyPI

Project links

Homepage

Project description

docs/source/_static/img/torchtext_logo.png

https://circleci.com/gh/pytorch/text.svg?style=svg

torchtext

This repository consists of:

torchtext.datasets: The raw text iterators for common NLP datasets
torchtext.data: Some basic NLP building blocks
torchtext.transforms: Basic text-processing transformations
torchtext.models: Pre-trained models
torchtext.vocab: Vocab and Vectors related classes and factory functions
examples: Example NLP workflows with PyTorch and torchtext library.

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.

Version Compatibility
PyTorch version	torchtext version	Supported Python version
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, 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
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).

Additionally, datasets in torchtext are implemented using the torchdata library. Please take a look at the installation instructions to download the latest nightlies or install from source.

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: 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
DistilRoBERTa
XLM-RoBERTa: Base and Large Architure
T5: Small, Base, Large, 3B, and 11B Architecture
Flan-T5: Base, Large, XL, and XXL Architecture

Tokenizers

The transforms module currently support following scriptable tokenizers:

Tutorials

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

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!

Project details

These details have been verified by PyPI

Maintainers

atalman malfet seemethere soumith

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

0.18.0

Apr 24, 2024

0.17.2

Mar 27, 2024

This version

0.17.1

Feb 22, 2024

0.17.0

Jan 30, 2024

0.16.2

Dec 14, 2023

0.16.1

Nov 15, 2023

0.16.0

Oct 4, 2023

0.15.2

May 8, 2023

0.15.1

Mar 15, 2023

0.15.0 yanked

Mar 15, 2023

Reason this release was yanked:

Contains an incorrect dependency on torch

0.14.1

Dec 15, 2022

0.14.0

Oct 28, 2022

0.13.1

Aug 5, 2022

0.13.0

Jun 28, 2022

0.12.0

Mar 10, 2022

0.11.2

Jan 27, 2022

0.11.1

Dec 15, 2021

0.11.0

Oct 21, 2021

0.10.1

Sep 20, 2021

0.10.0

Jun 15, 2021

0.9.1

Mar 25, 2021

0.9.0

Mar 4, 2021

0.8.1

Dec 10, 2020

0.8.0

Oct 27, 2020

0.7.0

Jul 28, 2020

0.6.0

Apr 21, 2020

0.5.0

Jan 15, 2020

0.4.0

Aug 8, 2019

0.3.1

Oct 11, 2018

0.2.3

Apr 9, 2018

0.2.1

Dec 28, 2017

0.2.0

Oct 20, 2017

0.1.1

Aug 14, 2017

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

torchtext-0.17.1-cp311-cp311-win_amd64.whl (1.9 MB view details)

Uploaded Feb 22, 2024 CPython 3.11 Windows x86-64

torchtext-0.17.1-cp311-cp311-manylinux1_x86_64.whl (2.0 MB view details)

Uploaded Feb 22, 2024 CPython 3.11

torchtext-0.17.1-cp311-cp311-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Feb 22, 2024 CPython 3.11 macOS 11.0+ ARM64

torchtext-0.17.1-cp311-cp311-macosx_10_13_x86_64.whl (2.3 MB view details)

Uploaded Feb 22, 2024 CPython 3.11 macOS 10.13+ x86-64

torchtext-0.17.1-cp310-cp310-win_amd64.whl (1.9 MB view details)

Uploaded Feb 22, 2024 CPython 3.10 Windows x86-64

torchtext-0.17.1-cp310-cp310-manylinux1_x86_64.whl (2.0 MB view details)

Uploaded Feb 22, 2024 CPython 3.10

torchtext-0.17.1-cp310-cp310-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Feb 22, 2024 CPython 3.10 macOS 11.0+ ARM64

torchtext-0.17.1-cp310-cp310-macosx_10_13_x86_64.whl (2.3 MB view details)

Uploaded Feb 22, 2024 CPython 3.10 macOS 10.13+ x86-64

torchtext-0.17.1-cp39-cp39-win_amd64.whl (1.9 MB view details)

Uploaded Feb 22, 2024 CPython 3.9 Windows x86-64

torchtext-0.17.1-cp39-cp39-manylinux1_x86_64.whl (2.0 MB view details)

Uploaded Feb 22, 2024 CPython 3.9

torchtext-0.17.1-cp39-cp39-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Feb 22, 2024 CPython 3.9 macOS 11.0+ ARM64

torchtext-0.17.1-cp39-cp39-macosx_10_13_x86_64.whl (2.3 MB view details)

Uploaded Feb 22, 2024 CPython 3.9 macOS 10.13+ x86-64

torchtext-0.17.1-cp38-cp38-win_amd64.whl (1.9 MB view details)

Uploaded Feb 22, 2024 CPython 3.8 Windows x86-64

torchtext-0.17.1-cp38-cp38-manylinux1_x86_64.whl (2.0 MB view details)

Uploaded Feb 22, 2024 CPython 3.8

torchtext-0.17.1-cp38-cp38-macosx_11_0_arm64.whl (2.1 MB view details)

Uploaded Feb 22, 2024 CPython 3.8 macOS 11.0+ ARM64

torchtext-0.17.1-cp38-cp38-macosx_10_13_x86_64.whl (2.3 MB view details)

Uploaded Feb 22, 2024 CPython 3.8 macOS 10.13+ x86-64

File details

Details for the file torchtext-0.17.1-cp311-cp311-win_amd64.whl.

File metadata

Download URL: torchtext-0.17.1-cp311-cp311-win_amd64.whl
Upload date: Feb 22, 2024
Size: 1.9 MB
Tags: CPython 3.11, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for torchtext-0.17.1-cp311-cp311-win_amd64.whl
Algorithm	Hash digest
SHA256	`30530f02b23c0cd52b1a4c375cecaf4d9918a556033891f0047973c8fb9674b9`
MD5	`6b2d5de04a3d12fcad1eb34fcce57902`
BLAKE2b-256	`b2cdadd336798f3ebfecaadf1f5aed211216ae9ecb6af3d82e62ecc0af9bbdd8`

Algorithm	Hash digest
SHA256	`d68701754082303dbcfeb177cb04055d66741672746a5b216a3621b14f491424`
MD5	`50bb54401b5094b4b999d7eebbada0d0`
BLAKE2b-256	`e499034b8a451109d8172afc81cb7fada7634b0db4a4bcfaf61188b61c819ea9`

Algorithm	Hash digest
SHA256	`9c17d7a9cd2217f0a944914bb2f47608efc627cc4ab5dc7ca1cf068a42d84d6c`
MD5	`2a8acc29b8e07b7a64ea2afa556e65c1`
BLAKE2b-256	`64ef5cf09110bd723826f095977920c79429f030411be71e305f69c88b8c6c3a`

Algorithm	Hash digest
SHA256	`62a3553bf5caba04a86ca5dd41b0e80f23a9e0fb414215372f8a3adef8f91dbe`
MD5	`c1cd3038d7dd4fd1cf6ff9937f59561e`
BLAKE2b-256	`fce364db5527f64c514ca3213863a7696b3dd8ddf8bd5a1642b2592feb91c5d0`

Algorithm	Hash digest
SHA256	`95170cdb941a02d26180bf8e64b22da4ee3131d98070789d0976e2800c08afa5`
MD5	`f04b64fc4acd43810c1a72474c597275`
BLAKE2b-256	`dba67337281cd27fa432a7c235d579e76d4e36f27344c63a3fc8e3ee72e03d1b`

Algorithm	Hash digest
SHA256	`c02f59e6f99346cf92970cf023ce5d0984fb07488bb775933dd6039bf054c392`
MD5	`dbcfa0c69bcb16433940c14290dcdd9e`
BLAKE2b-256	`b3d2332d85dc3529c573dc03b3a888b52b70702e0293d3d4e17c7161b1f7a0d0`

Algorithm	Hash digest
SHA256	`b404312a6ca46b7a487b7f4b00f3c85573553d6d4a4b46412c3164154a248a1f`
MD5	`73dd3796823be1b3ce82f2e9efe6a2e2`
BLAKE2b-256	`a8d3d804bc464885112eb2710592afd98eed227e6956a622b3d0421d616d833c`

Algorithm	Hash digest
SHA256	`769b563e5e3a0ebb13759459f65396a6cbc24f281d8faaf94ff1597ded26da11`
MD5	`e615bdaaa8604853c28aad479bd5d2f1`
BLAKE2b-256	`83004164b7dfe20114c62dbaa335a246190b4dd354d0e022a9befa9afd652860`

Algorithm	Hash digest
SHA256	`e5867499e1572231535fcaf08b696b42c3fe73b406776999f6f4fb6d197d1deb`
MD5	`3016142934e5893cfe200572eeb35fd4`
BLAKE2b-256	`e983b80cafbf582335e6b60b38a70360c0230bd39588dd8c56edc118ca4d487d`

Algorithm	Hash digest
SHA256	`c7dc3ebac4b060e01abeb6bf8575c3a79e4f7f0b0701d1b683e389d3090e3c81`
MD5	`9576385908127aae7c025028f56f1d38`
BLAKE2b-256	`e7a08c0828a5f84c5abbd6a01a2779a85286138fe1bd248d6de36fbdef4f1bab`

Algorithm	Hash digest
SHA256	`92527ab9d452030663e1ff292dbd85935fbdc288197b3e4673ead92913dc2249`
MD5	`070983c99eac26e02bf2aaa38b291ab2`
BLAKE2b-256	`0715ddd33ffe7608202a80fbfcef8828061256485a328b4abc9d59492e2ca76c`

Algorithm	Hash digest
SHA256	`a5896f9cbf9acd11619c1c444d8e78d8e3d250d53a56b521619b83587dbd4972`
MD5	`244f4b8b7d8c839d626356467c3804e2`
BLAKE2b-256	`60a84e4a612659d6a7dd9483e822e3b0b87a0e44efbf65f50ccda2e3e9ccc824`

Algorithm	Hash digest
SHA256	`e4d71c6dc27dc1532f5d7687f9ed037f44d55d333cd84368ab7aaa435aab3a20`
MD5	`17c5b6840990d51fc415b285a1559868`
BLAKE2b-256	`e62da16547ff289542e05bb1dc3ad613c990261c2e6d58077e5bc936164a2a93`

Algorithm	Hash digest
SHA256	`087d7195cf1c541cdb0f7f218f4f3480d5d3a6f889c6f01b786fe5edd7f0b088`
MD5	`d7abf950690a7375ae3e76993c7103c8`
BLAKE2b-256	`cfb0b6338676f1545fbe88001b38f016dbc551e298b136412bdaa3fa2c1981c0`

Algorithm	Hash digest
SHA256	`0c13f45175e596eba668bd772b6db76ce56b123abca178ae17d6bccaede97c3b`
MD5	`6e21f3db69402bab7459ab7257970080`
BLAKE2b-256	`bc80b33b22f06f2ee6521c1fdc7ac4aa20ee2f745099da132901888db95e9295`

Algorithm	Hash digest
SHA256	`1dde7a2f1bb4e2642c879a12f18223ae32251d4e419de3f90f3e3b9d6453672e`
MD5	`da1704382ce9abdef9068360d128e393`
BLAKE2b-256	`b45cf8865f5c6b1cfec68911aaf0b79e0310a067b8ac161fe954b5aaec2faffc`

torchtext 0.17.1

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

torchtext

Installation

Optional requirements

Building from source

Documentation

Datasets

Models

Tokenizers

Tutorials

Disclaimer on Datasets

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata