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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- Officially Supported Models
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If you're interested in using AllenNLP for model development, we recommend you check out the AllenNLP Guide. When you're ready to start your project, we've created a couple of template repositories that you can use as a starting place:
- If you want to use
allennlp trainand config files to specify experiments, use this template. We recommend this approach. - If you'd prefer to use python code to configure your experiments and run your training loop, use this template. There are a few things that are currently a little harder in this setup (loading a saved model, and using distributed training), but otherwise it's functionality equivalent to the config files setup.
In addition, there are external tutorials:
And others on the AI2 AllenNLP blog.
| 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 |
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.
We support AllenNLP on Mac and Linux environments. We presently do not support Windows but are open to contributions.
Conda can be used set up a virtual environment with the version of Python required for AllenNLP. If you already have a Python 3 environment you want to use, you can skip to the 'installing via pip' section.
-
Create a Conda environment with Python 3.7 (3.6 or 3.8 would work as well):
conda create -n allennlp python=3.7 -
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 is simple using pip.
pip install allennlpLooking for bleeding edge features? You can install nightly releases directly from pypi
AllenNLP installs a script when you install the python package, so you can run allennlp commands just by typing allennlp into a terminal. For example, you can now test your installation with allennlp test-install.
You may also want to install allennlp-models, which contains the NLP constructs to train and run our officially
supported models, many of which are hosted at https://demo.allennlp.org.
pip install allennlp-modelsDocker 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:latestYou can test the Docker environment with
docker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:latest test-install You can also install AllenNLP by cloning our git repository:
git clone https://github.com/allenai/allennlp.gitCreate a Python 3.7 virtual environment, and install AllenNLP in editable mode by running:
pip install --editable .
pip install -r dev-requirements.txtThis 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.
See https://github.com/allenai/allennlp-models
for instructions on installing allennlp-models from source.
Once you've installed AllenNLP, you can run the command-line interface
with the allennlp command (whether you installed from pip or from source).
allennlp has various subcommands such as train, evaluate, and predict.
To see the full usage information, run allennlp --help.
AllenNLP releases Docker images to Docker Hub for each release. For information on how to run these releases, see Installing using Docker.
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
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.
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.
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.
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},
}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.