Repository files navigation

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.

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

Using conda:

conda install -c pytorch torchtext

Using pip:

pip install torchtext

If you want to use English tokenizer fromSpaCy, you need to install SpaCy and download its English model:

pip install spacypython -m spacy download en_core_web_sm

Alternatively, you might want to use theMoses tokenizer port inSacreMoses (split fromNLTK). You have to install SacreMoses:

pip install sacremoses

For torchtext 0.5 and below,sentencepiece:

conda install -c powerai sentencepiece

To build torchtext from source, you needgit,CMake and C++11 compiler such asg++.:

git clone https://github.com/pytorch/text torchtextcd torchtextgit submodule update --init --recursive# Linuxpython setup.py clean install# OSXCC=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 withconda (here) andpip (here).

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

Find the documentationhere.

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

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

The transforms module currently support following scriptable tokenizers:

• SentencePiece
• GPT-2 BPE
• CLIP
• RE2
• BERT

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

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!