Text utilities and datasets for PyTorch

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for atalman from gravatar.com atalman Avatar for malfet from gravatar.com malfet Avatar for seemethere from gravatar.com seemethere Avatar for soumith from gravatar.com soumith

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Project description

https://circleci.com/gh/pytorch/text.svg?style=svg https://codecov.io/gh/pytorch/text/branch/main/graph/badge.svg 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

torchtext

This repository consists of:

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.

Version Compatibility

PyTorch version

torchtext version

Supported Python version

nightly build

main

>=3.6, <=3.9

1.9

0.10

>=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
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.

[Prototype] Experimental Code

We have re-written several building blocks under torchtext.experimental:

  • Transforms: some basic data processing building blocks

  • Vectors: the vectors to convert tokens into tensors.

These prototype building blocks in the experimental folder are available in the nightly release only. The nightly packages are accessible via Pip and Conda for Windows, Mac, and Linux. For example, Linux users can install the nightly wheels with the following command:

pip install --pre --upgrade torch torchtext -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html

For more detailed instructions, please refer to Install PyTorch. It should be noted that the new building blocks are still under development, and the APIs have not been solidified.

[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!

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for atalman from gravatar.com atalman Avatar for malfet from gravatar.com malfet Avatar for seemethere from gravatar.com seemethere Avatar for soumith from gravatar.com soumith

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

0.18.0

0.17.2

0.17.1

0.17.0

0.16.2

0.16.1

0.16.0

0.15.2

0.15.1

0.15.0 yanked

Reason this release was yanked:

Contains an incorrect dependency on torch

0.14.1

0.14.0

0.13.1

0.13.0

0.12.0

0.11.2

0.11.1

This version

0.11.0

0.10.1

0.10.0

0.9.1

0.9.0

0.8.1

0.8.0

0.7.0

0.6.0

0.5.0

0.4.0

0.3.1

0.2.3

0.2.1

0.2.0

0.1.1

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-0.11.0-cp39-cp39-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.9 Windows x86-64

torchtext-0.11.0-cp39-cp39-manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded CPython 3.9

torchtext-0.11.0-cp39-cp39-manylinux1_x86_64.whl (8.0 MB view details)

Uploaded CPython 3.9

torchtext-0.11.0-cp39-cp39-macosx_10_9_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

torchtext-0.11.0-cp38-cp38-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.8 Windows x86-64

torchtext-0.11.0-cp38-cp38-manylinux2014_aarch64.whl (9.8 MB view details)

Uploaded CPython 3.8

torchtext-0.11.0-cp38-cp38-manylinux1_x86_64.whl (8.0 MB view details)

Uploaded CPython 3.8

torchtext-0.11.0-cp38-cp38-macosx_10_9_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

torchtext-0.11.0-cp37-cp37m-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.7m Windows x86-64

torchtext-0.11.0-cp37-cp37m-manylinux2014_aarch64.whl (9.9 MB view details)

Uploaded CPython 3.7m

torchtext-0.11.0-cp37-cp37m-manylinux1_x86_64.whl (8.0 MB view details)

Uploaded CPython 3.7m

torchtext-0.11.0-cp37-cp37m-macosx_10_9_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

torchtext-0.11.0-cp36-cp36m-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.6m Windows x86-64

torchtext-0.11.0-cp36-cp36m-manylinux2014_aarch64.whl (9.9 MB view details)

Uploaded CPython 3.6m

torchtext-0.11.0-cp36-cp36m-manylinux1_x86_64.whl (8.0 MB view details)

Uploaded CPython 3.6m

torchtext-0.11.0-cp36-cp36m-macosx_10_9_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file torchtext-0.11.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 9456f89a607ce9a1a14aebfe573812c560720bc72c4856ba5197ceff1ac4c3df
MD5 ef53a60f901e0d5129c51665afdd7c71
BLAKE2b-256 582f8dc4343cd1cfd5ffefc8cf86127070cdc6134cdc2bd2d87fd5b2477fc545

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp39-cp39-manylinux2014_aarch64.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.9
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 293a58f55bb35b19a44e9f47f1be826266435f8aa243423bb67d349ee5ae031e
MD5 a43d02cdffeb2349943b5831e16f636e
BLAKE2b-256 5308e107307176179550badb8dcadc20e43ca8031896fdb79218a45abc8f2bba

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp39-cp39-manylinux1_x86_64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp39-cp39-manylinux1_x86_64.whl
  • Upload date:
  • Size: 8.0 MB
  • Tags: CPython 3.9
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp39-cp39-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 bc5f8c44f62a87dd3f91bab19a2d91c7c43b22f81b7a20ece52f97c55774eee3
MD5 f63806f2d6c48a1398f8826b3bbd09a0
BLAKE2b-256 4ba35e267e4af4df50d7a8a6d42a08a489ba5e363bf3867dcdc2b38ab524ddaa

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a21473c11e70beddda234055ae7e46eed317ca5977bdb2b2735b9e67a3617c23
MD5 52ef10047d87896e09a21b36dc22f1a1
BLAKE2b-256 4551ad1d18d9d55053f9010a9d56d42152d2b7a2487d8fc099d1b4dfd2424fec

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 0f9e1d403bd368e2dd9f35dacaf8f982f9a379d5d3a66954f89efef17dc2726a
MD5 3c9d664d54885a55de00d82acc8d97bb
BLAKE2b-256 bab87b4202bba32f460be8e12b53dbb08646825fb9030e5194d35a1c3004dd4e

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp38-cp38-manylinux2014_aarch64.whl
  • Upload date:
  • Size: 9.8 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c6ae98b822882dfccbb7ae7fa979ac401ae15e0a8eb873818032dc8bd440dab5
MD5 cf0d76e35c45ea93f9b2b6707c3c351c
BLAKE2b-256 0415a5a8ba647d106ad21d017cba2f62edc9aa88e5015fec4aa5ca0bdd11764b

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 8.0 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 195fe7940a2b57dff2a1e38b8a3826eaecb92138653fb4a769a19b6fde8a96e3
MD5 d7fd940a339295bf14966d17199ebc1c
BLAKE2b-256 3acce9a1b1496931c052017a6b5043f09560fce8def13be39220fbdabb5d43f2

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 424574fefcd021a17977c9de6f7ba3c85eddbeeed4b10bad7400de6c32ad60da
MD5 7f45c759dc769cd8cb65bd3e765c83f7
BLAKE2b-256 9765ed91bf271d77845d1a92f749fceb3fc45a18d90eaebdd00547ffdbdf06a4

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 83ffe0dcb7894ceffaa3b40410ad1cb4b92cd7cf19ea2ab0fdb1dfbedd7a4acd
MD5 b676b1ef0e8bc7dd66f629238ed4d972
BLAKE2b-256 094a851253eae5b15bdf16aac576df7dd910d0c236069a19bb4e93074961590e

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp37-cp37m-manylinux2014_aarch64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp37-cp37m-manylinux2014_aarch64.whl
  • Upload date:
  • Size: 9.9 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp37-cp37m-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e9020e999e0b50af3167b3f7762b5893dcde6aa78f750e709f2e5513ba5cbf28
MD5 abe652a4d4630fd1dc62b7cbf0d56422
BLAKE2b-256 0bc498d4d937e44f456b9ec6548969f4f7495d14faac0115f9a0f5981d4d0e38

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 8.0 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 5bf4ce9b7379999dfa11e123c82de259284f25566bd7a8885d3b31a12c37a3e7
MD5 5720fdda4a88c4e284384b11428d86d7
BLAKE2b-256 de5f8175de5969e72d425f2ad95d02fc25b4fd3d66487cb0f9db15d3374bd181

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 81aaf39aa332b7965dcbfa573f6fcb25eaebb6cc59d064d56ffe4dfb1339982c
MD5 4e34d54585ddb164c51fe8e2409535c4
BLAKE2b-256 802736a0a6e4f80bdedd9f3bdfd8857e0b67e6cd9131a6790e0f010e9af927d0

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 1.4 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 f4d04a72a69dff2be236b4ac4b4dd1d438f3c4a3210d563f6d6af303ae202bc9
MD5 3cb5bd17ac145420d29781f78dcd317e
BLAKE2b-256 f6c82fd9d0cb2f1e9c5d0d112694721e7c2c777ec09c1f86471cdee656bb8209

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp36-cp36m-manylinux2014_aarch64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp36-cp36m-manylinux2014_aarch64.whl
  • Upload date:
  • Size: 9.9 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp36-cp36m-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f73e37d9a00c395370b597df8668c107d81273ad6d835fe9970f5d8eff95a7ea
MD5 8bc8fdf25fdca01d7a7919857d6b0023
BLAKE2b-256 c305a3fc104b7e6e4e254b6ee52a64ba20cb1432f71b28ea9fef04c35ed8b0f7

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 8.0 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 4c15a107afac0487cf0c135dbe777090a095f33e34e5f82eef7e559ca1981102
MD5 7807e756210ffaa1ba089d73812a6fc1
BLAKE2b-256 05642d2bd1281d7303abbbbfba546f14d48daa8f97732950c5219b73d0c3a71e

See more details on using hashes here.

Provenance

File details

Details for the file torchtext-0.11.0-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: torchtext-0.11.0-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.24.0 setuptools/50.3.1.post20201107 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.5

File hashes

Hashes for torchtext-0.11.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 90c3255c3f31e6a64c52a4a729ce22d43b98cbb22ceae5fb0a55670734fcbf46
MD5 6aaf792109a58ee207162e62376e40f1
BLAKE2b-256 365b06ce7cdc965f35bc739f502d451bfdc46f1fc072b6cfbb1039ca2cd13486

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page