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.
| 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.service | a web server to that can serve demos for your models |
| 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.
Windows is currently not officially supported, although we try to fix issues when they are easily addressed.
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.
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Create a Conda environment with Python 3.6
conda create -n allennlp python=3.6
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Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use AllenNLP.
source activate allennlp
Installing the library and dependencies is simple using pip.
pip install allennlpThat'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.
pip currently installs Pytorch for CUDA 9 only (or no GPU). If you require an older version,
please visit https://pytorch.org/ and install the relevant pytorch binary.
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:v0.8.4You can test the Docker environment with docker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:v0.8.4 test-install.
You can also install AllenNLP by cloning our git repository:
git clone https://github.com/allenai/allennlp.gitCreate a Python 3.6 virtual environment, and install AllenNLP in editable mode by running:
pip install --editable .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.
The full development environment also requires the JVM and perl,
which must be installed separately. ./scripts/verify.py will run
the full suite of tests used by our continuous build environment.
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:
configure Run the configuration wizard.
train Train a model.
evaluate Evaluate the specified model + dataset.
predict Use a trained model to make predictions.
make-vocab Create a vocabulary.
elmo Create word vectors using a pretrained ELMo model.
fine-tune Continue training a model on a new dataset.
dry-run Create a vocabulary, compute dataset statistics and other
training utilities.
test-install
Run the unit tests.
find-lr Find a learning rate range.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 run the following command (it will take some time, as it completely builds the environment needed to run AllenNLP.)
docker build -f Dockerfile.pip --tag allennlp/allennlp:latest .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. We allow users a two week window to follow up on questions, after which we will close issues. They can be re-opened if there is further discussion.
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.