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GluonNLP: Your Choice of Deep Learning for NLP

GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research.

Features

For NLP Practitioners

  • Easy-to-use Data Pipeline
  • Automatically Train Models via AutoNLP (TODO)

For Researchers

  • Pretrained Model Zoo
  • Programming with numpy-like API

For Engineers

  • Fast Deployment
  • AWS Integration

Installation

First of all, install the latest MXNet. You may use the following commands:

# Install the version with CUDA 10.0
python3 -m pip install -U --pre "mxnet-cu100>=2.0.0b20200802" -f https://dist.mxnet.io/python

# Install the version with CUDA 10.1
python3 -m pip install -U --pre "mxnet-cu101>=2.0.0b20200802" -f https://dist.mxnet.io/python

# Install the version with CUDA 10.2
python3 -m pip install -U --pre "mxnet-cu102>=2.0.0b20200802" -f https://dist.mxnet.io/python

# Install the cpu-only version
python3 -m pip install -U --pre "mxnet>=2.0.0b20200802" -f https://dist.mxnet.io/python

To install GluonNLP, use

python3 -m pip install -U -e .

# Also, you may install all the extra requirements via
python3 -m pip install -U -e ."[extras]"

If you find that you do not have the permission, you can also install to the user folder:

python3 -m pip install -U -e . --user

For Windows users, we recommend to use the Windows Subsystem for Linux.

Access the Command-line Toolkits

To facilitate the researcher and the engineers, we provide command-line-toolkits for downloading and preprocessing the NLP datasets. For more details, you may refer to GluonNLP Datasets and GluonNLP Preprocessing Tools.

# CLI for downloading / preparing the dataset
nlp_data help

# CLI for accessing some common data preprocessing scripts
nlp_preprocess help

# Also, you can use `python -m` to access the toolkits
python3 -m gluonnlp.cli.data help
python3 -m gluonnlp.cli.preprocess help

Frequently Asked Questions

  • Question: I cannot you access the command line toolkits. By running nlp_data, it reports nlp_data: command not found.

    This is sometimes because that you have installed glunonnlp to the user folder and the executables are installed to ~/.local/bin. You can try to change the PATH variable to also include '~/.local/bin'.

    export PATH=${PATH}:~/.local/bin

Run Unittests

You may go to tests to see all how to run the unittests.

Use Docker

You can use Docker to launch a JupyterLab development environment with GluonNLP installed.

docker pull gluonai/gluon-nlp:gpu-latest
docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 --shm-size=4g gluonai/gluon-nlp:gpu-latest

For more details, you can refer to the guidance in tools/docker.