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 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.
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 ./scripts/verify.py.
pip currently installs Pytorch for CUDA 9 only (or no GPU). If you require an older version,
please visit http://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.
docker run -it -p 8000:8000 --rm allennlp/allennlp:v0.6.1` You can now test your installation with ./scripts/verify.py.
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 the necessary requirements by running:
INSTALL_TEST_REQUIREMENTS=true scripts/install_requirements.shChanging the flag to false if you don't want to be able to run tests. Once the requirements have been installed, run:
pip install --editable .To install the AllenNLP library in editable mode into your
environment. 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 ./scripts/verify.py.
Once you've installed AllenNLP, you can run the command-line interface either
with the allennlp command (if you installed via pip) or bin/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 Generate configuration stubs.
train Train a model
evaluate Evaluate the specified model + dataset
predict Use a trained model to make predictions.
make-vocab Create a vocabulary
elmo Use a trained model to make predictions.
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.Built on PyTorch, AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP was designed with the following principles:
- Hyper-modular and lightweight. Use the parts which you like seamlessly with PyTorch.
- Extensively tested and easy to extend. Test coverage is above 90% and the example models provide a template for contributions.
- Take padding and masking seriously, making it easy to implement correct models without the pain.
- Experiment friendly. Run reproducible experiments from a json specification with comprehensive logging.
AllenNLP includes reference implementations of high quality models for Semantic Role Labelling, Question and Answering (BiDAF), Entailment (decomposable attention), and more (see http://www.allennlp.org/models).
AllenNLP is built and maintained by the Allen Institute for Artificial Intelligence, in close collaboration with researchers at the University of Washington and elsewhere. With a dedicated team of best-in-field researchers and software engineers, the AllenNLP project is uniquely positioned to provide state of the art models with high quality engineering.
| 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 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 ./scripts/verify.py.
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.