allennlp 0.7.2

An open-source NLP research library, built on PyTorch.

<p align="center"><img width="40%" src="doc/static/allennlp-logo-dark.png" /></p>

[/statusIcon)](http://build.allennlp.org/viewType.html?buildTypeId=AllenNLP_AllenNLPCommits&guest=1)
[](https://codecov.io/gh/allenai/allennlp)

An [Apache 2.0](https://github.com/allenai/allennlp/blob/master/LICENSE) NLP research library, built on PyTorch,
for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.

## Quick Links

* [Website](http://www.allennlp.org/)
* [Tutorial](https://allennlp.org/tutorials)
* [Documentation](https://allenai.github.io/allennlp-docs/)
* [Contributing Guidelines](CONTRIBUTING.md)
* [Model List](MODELS.md)
* [Continuous Build](http://build.allennlp.org/)

## Package Overview

<table>
<tr>
<td><b> allennlp </b></td>
<td> an open-source NLP research library, built on PyTorch </td>
</tr>
<tr>
<td><b> allennlp.commands </b></td>
<td> functionality for a CLI and web service </td>
</tr>
<tr>
<td><b> allennlp.data </b></td>
<td> a data processing module for loading datasets and encoding strings as integers for representation in matrices </td>
</tr>
<tr>
<td><b> allennlp.models </b></td>
<td> a collection of state-of-the-art models </td>
</tr>
<tr>
<td><b> allennlp.modules </b></td>
<td> a collection of PyTorch modules for use with text </td>
</tr>
<tr>
<td><b> allennlp.nn </b></td>
<td> tensor utility functions, such as initializers and activation functions </td>
</tr>
<tr>
<td><b> allennlp.service </b></td>
<td> a web server to that can serve demos for your models </td>
</tr>
<tr>
<td><b> allennlp.training </b></td>
<td> functionality for training models </td>
</tr>
</table>

## 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](https://conda.io/) 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](https://conda.io/docs/download.html).

2. Create a Conda environment with Python 3.6

```bash
conda create -n allennlp python=3.6
```

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

```bash
source activate allennlp
```

#### Installing the library and dependencies

Installing the library and dependencies is simple using `pip`.

```bash
pip install allennlp
```

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`.

_`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._

### 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](https://docs.docker.com/engine/installation/)
just run the following command to get an environment that will run on either the cpu or gpu.

```bash
docker run -it -p 8000:8000 --rm allennlp/allennlp:v0.7.2
```

You can test the Docker environment with `docker run -it -p 8000:8000 --rm allennlp/allennlp:v0.7.2 test-install`.

### Installing from source

You can also install AllenNLP by cloning our git repository:

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

Create a Python 3.6 virtual environment, and install the necessary requirements by running:

```bash
INSTALL_TEST_REQUIREMENTS=true scripts/install_requirements.sh
```

Changing the flag to false if you don't want to be able to run
tests. Once the requirements have been installed, run:

```bash
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 `bin/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.

## 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 `bin/allennlp` (if you installed via source).

```bash
$ 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.
```

## Docker images

AllenNLP releases Docker images to [Docker Hub](https://hub.docker.com/r/allennlp/) for each release. For information on how to run these releases, see [Installing using Docker](#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](https://www.docker.com/get-started).
Then run the following command
(it will take some time, as it completely builds the
environment needed to run AllenNLP.)

```bash
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
```

### 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`.

## Citing

If you use AllenNLP in your research, please cite [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://www.semanticscholar.org/paper/AllenNLP%3A-A-Deep-Semantic-Natural-Language-Platform-Gardner-Grus/a5502187140cdd98d76ae711973dbcdaf1fef46d).

```
@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)](http://www.allenai.org).
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](https://github.com/allenai/allennlp/graphs/contributors) page.