An Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.

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Quick Links

• Website
• Tutorial
• Forum
• Documentation ( latest | stable | master )
• Contributing Guidelines
• Pretrained Models
• Continuous Build
• Nightly Releases
• Officially Supported Models

Package Overview

allennlp	an open-source NLP research library, built on PyTorch
allennlp.commands	functionality for a CLI and web service
allennlp.data	a data processing module for loading datasets and encoding strings as integers for representation in matrices
allennlp.models	a collection of state-of-the-art models
allennlp.modules	a collection of PyTorch modules for use with text
allennlp.nn	tensor utility functions, such as initializers and activation functions
allennlp.training	functionality for training models

Installation

AllenNLP requires Python 3.6.1 or later. The preferred way to install AllenNLP is via pip. Just run pip install allennlp in your Python environment and you're good to go!

If you need pointers on setting up an appropriate Python environment or would like to install AllenNLP using a different method, see below.

Windows is currently not officially supported, although we try to fix issues when they are easily addressed.

Installing via pip

Setting up a virtual environment

Conda can be used set up a virtual environment with the version of Python required for AllenNLP. If you already have a Python 3.6 or 3.7 environment you want to use, you can skip to the 'installing via pip' section.

1. Download and install Conda.

2. Create a Conda environment with Python 3.7

   conda create -n allennlp python=3.7

3. Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use AllenNLP.

   conda activate allennlp

Installing the library and dependencies

Installing the library and dependencies is simple using pip.

pip install allennlp

Looking for bleeding edge features? You can install nightly releases directly from pypi That's it! You're now ready to build and train AllenNLP models. AllenNLP installs a script when you install the python package, meaning you can run allennlp commands just by typing allennlp into a terminal.

You can now test your installation with allennlp test-install.

Installing using Docker

Docker provides a virtual machine with everything set up to run AllenNLP-- whether you will leverage a GPU or just run on a CPU. Docker provides more isolation and consistency, and also makes it easy to distribute your environment to a compute cluster.

Once you have installed Docker just run the following command to get an environment that will run on either the cpu or gpu.

mkdir -p $HOME/.allennlp/
docker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:latest

You can test the Docker environment with docker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:latest test-install.

Installing from source

You can also install AllenNLP by cloning our git repository:

git clone https://github.com/allenai/allennlp.git

Create a Python 3.7 virtual environment, and install AllenNLP in editable mode by running:

pip install --editable .
pip install -r dev-requirements.txt

This will make allennlp available on your system but it will use the sources from the local clone you made of the source repository.

You can test your installation with allennlp test-install.

Running AllenNLP

Once you've installed AllenNLP, you can run the command-line interface either with the allennlp command (if you installed via pip) or allennlp (if you installed via source).

$ allennlp
Run AllenNLP

optional arguments:
  -h, --help     show this help message and exit
  --version      show program's version number and exit

Commands:
  
    elmo         Create word vectors using a pretrained ELMo model.
    evaluate     Evaluate the specified model + dataset.
    find-lr      Find a learning rate range.
    predict      Use a trained model to make predictions.
    print-results
                 Print results from allennlp serialization directories to the
                 console.
    test-install
                 Run the unit tests.
    train        Train a model.

Docker images

AllenNLP releases Docker images to Docker Hub for each release. For information on how to run these releases, see Installing using Docker.

Building a Docker image

For various reasons you may need to create your own AllenNLP Docker image. The same image can be used either with a CPU or a GPU.

First, you need to install Docker. Then you will need a wheel of allennlp in the dist/ directory. You can either obtain a pre-built wheel from a PyPI release or build a new wheel from source.

PyPI release wheels can be downloaded by going to https://pypi.org/project/allennlp/#history, clicking on the desired release, and then clicking "Download files" in the left sidebar. After downloading, make you sure you put the wheel in the dist/ directory (which may not exist if you haven't built a wheel from source yet).

To build a wheel from source, just run python setup.py wheel.

Before building the image, make sure you only have one wheel in the dist/ directory.

Once you have your wheel, run make docker-image. By default this builds an image with the tag allennlp/allennlp. You can change this to anything you want by setting the DOCKER_TAG flag when you call make. For example, make docker-image DOCKER_TAG=my-allennlp.

You should now be able to see this image listed by running docker images allennlp.

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
allennlp/allennlp   latest              b66aee6cb593        5 minutes ago       2.38GB

Running the Docker image

You can run the image with docker run --rm -it allennlp/allennlp:latest. The --rm flag cleans up the image on exit and the -it flags make the session interactive so you can use the bash shell the Docker image starts.

You can test your installation by running allennlp test-install.

Issues

Everyone is welcome to file issues with either feature requests, bug reports, or general questions. As a small team with our own internal goals, we may ask for contributions if a prompt fix doesn't fit into our roadmap. To keep things tidy we will often close issues we think are answered, but don't hesitate to follow up if further discussion is needed.

Contributions

The AllenNLP team at AI2 (@allenai) welcomes contributions from the greater AllenNLP community, and, if you would like to get a change into the library, this is likely the fastest approach. If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. This will prevent you from spending significant time on an implementation which has a technical limitation someone could have pointed out early on. Small contributions can be made directly in a pull request.

Pull requests (PRs) must have one approving review and no requested changes before they are merged. As AllenNLP is primarily driven by AI2 (@allenai) we reserve the right to reject or revert contributions that we don't think are good additions.

Citing

If you use AllenNLP in your research, please cite AllenNLP: A Deep Semantic Natural Language Processing Platform.

@inproceedings{Gardner2017AllenNLP,
  title={AllenNLP: A Deep Semantic Natural Language Processing Platform},
  author={Matt Gardner and Joel Grus and Mark Neumann and Oyvind Tafjord
    and Pradeep Dasigi and Nelson F. Liu and Matthew Peters and
    Michael Schmitz and Luke S. Zettlemoyer},
  year={2017},
  Eprint = {arXiv:1803.07640},
}

Team

AllenNLP is an open-source project backed by the Allen Institute for Artificial Intelligence (AI2). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering. To learn more about who specifically contributed to this codebase, see our contributors page.