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        "author": "Allen Institute for Artificial Intelligence",
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            "Development Status :: 3 - Alpha",
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        "description": "<p align=\"center\"><img width=\"40%\" src=\"doc/static/allennlp-logo-dark.png\" /></p>\n\n[![Build Status](http://build.allennlp.org/app/rest/builds/buildType:(id:AllenNLP_AllenNLPCommits)/statusIcon)](http://build.allennlp.org/viewType.html?buildTypeId=AllenNLP_AllenNLPCommits&guest=1)\n[![codecov](https://codecov.io/gh/allenai/allennlp/branch/master/graph/badge.svg)](https://codecov.io/gh/allenai/allennlp)\n\nAn [Apache 2.0](https://github.com/allenai/allennlp/blob/master/LICENSE) NLP research library, built on PyTorch,\nfor developing state-of-the-art deep learning models on a wide variety of linguistic tasks.\n\n## Quick Links\n\n* [Website](https://allennlp.org/)\n* [Tutorial](https://allennlp.org/tutorials)\n* [Forum](https://discourse.allennlp.org)\n* [Documentation](https://allenai.github.io/allennlp-docs/)\n* [Contributing Guidelines](CONTRIBUTING.md)\n* [Model List](MODELS.md)\n* [Continuous Build](http://build.allennlp.org/)\n\n## Package Overview\n\n<table>\n<tr>\n    <td><b> allennlp </b></td>\n    <td> an open-source NLP research library, built on PyTorch </td>\n</tr>\n<tr>\n    <td><b> allennlp.commands </b></td>\n    <td> functionality for a CLI and web service </td>\n</tr>\n<tr>\n    <td><b> allennlp.data </b></td>\n    <td> a data processing module for loading datasets and encoding strings as integers for representation in matrices </td>\n</tr>\n<tr>\n    <td><b> allennlp.models </b></td>\n    <td> a collection of state-of-the-art models </td>\n</tr>\n<tr>\n    <td><b> allennlp.modules </b></td>\n    <td> a collection of PyTorch modules for use with text </td>\n</tr>\n<tr>\n    <td><b> allennlp.nn </b></td>\n    <td> tensor utility functions, such as initializers and activation functions </td>\n</tr>\n<tr>\n    <td><b> allennlp.service </b></td>\n    <td> a web server to that can serve demos for your models </td>\n</tr>\n<tr>\n    <td><b> allennlp.training </b></td>\n    <td> functionality for training models </td>\n</tr>\n</table>\n\n## Installation\n\nAllenNLP 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!\n\nIf you need pointers on setting up an appropriate Python environment or would like to install AllenNLP using a different method, see below.\n\nWindows is currently not officially supported, although we try to fix issues when they are easily addressed.\n\n### Installing via pip\n\n#### Setting up a virtual environment\n\n[Conda](https://conda.io/) can be used set up a virtual environment with the\nversion of Python required for AllenNLP.  If you already have a Python 3.6 or 3.7\nenvironment you want to use, you can skip to the 'installing via pip' section.\n\n1.  [Download and install Conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html).\n\n2.  Create a Conda environment with Python 3.6\n\n    ```bash\n    conda create -n allennlp python=3.6\n    ```\n\n3.  Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use AllenNLP.\n\n    ```bash\n    source activate allennlp\n    ```\n\n#### Installing the library and dependencies\n\nInstalling the library and dependencies is simple using `pip`.\n\n   ```bash\n   pip install allennlp\n   ```\n\nThat's it! You're now ready to build and train AllenNLP models.\nAllenNLP installs a script when you install the python package, meaning you can run allennlp commands just by typing `allennlp` into a terminal.\n\nYou can now test your installation with `allennlp test-install`.\n\n_`pip` currently installs Pytorch for CUDA 9 only (or no GPU). If you require an older version,\nplease visit https://pytorch.org/ and install the relevant pytorch binary._\n\n### Installing using Docker\n\nDocker provides a virtual machine with everything set up to run AllenNLP--\nwhether you will leverage a GPU or just run on a CPU.  Docker provides more\nisolation and consistency, and also makes it easy to distribute your\nenvironment to a compute cluster.\n\nOnce you have [installed Docker](https://docs.docker.com/engine/installation/)\njust run the following command to get an environment that will run on either the cpu or gpu.\n\n   ```bash\n   mkdir -p $HOME/.allennlp/\n   docker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:v0.9.0\n   ```\n\nYou can test the Docker environment with `docker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:v0.9.0 test-install`.\n\n### Installing from source\n\nYou can also install AllenNLP by cloning our git repository:\n\n  ```bash\n  git clone https://github.com/allenai/allennlp.git\n  ```\n\nCreate a Python 3.6 virtual environment, and install AllenNLP in `editable` mode by running:\n\n  ```bash\n  pip install --editable .\n  ```\n\nThis will make `allennlp` available on your system but it will use the sources from the local clone\nyou made of the source repository.\n\nYou can test your installation with `allennlp test-install`.\nThe full development environment also requires the JVM and `perl`,\nwhich must be installed separately.  `./scripts/verify.py` will run\nthe full suite of tests used by our continuous build environment.\n\n## Running AllenNLP\n\nOnce you've installed AllenNLP, you can run the command-line interface either\nwith the `allennlp` command (if you installed via `pip`) or `allennlp` (if you installed via source).\n\n```bash\n$ allennlp\nRun AllenNLP\n\noptional arguments:\n  -h, --help    show this help message and exit\n  --version     show program's version number and exit\n\nCommands:\n\n    configure   Run the configuration wizard.\n    train       Train a model.\n    evaluate    Evaluate the specified model + dataset.\n    predict     Use a trained model to make predictions.\n    make-vocab  Create a vocabulary.\n    elmo        Create word vectors using a pretrained ELMo model.\n    fine-tune   Continue training a model on a new dataset.\n    dry-run     Create a vocabulary, compute dataset statistics and other\n                training utilities.\n    test-install\n                Run the unit tests.\n    find-lr     Find a learning rate range.\n```\n\n## Docker images\n\nAllenNLP 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).\n\n### Building a Docker image\n\nFor various reasons you may need to create your own AllenNLP Docker image.\nThe same image can be used either with a CPU or a GPU.\n\nFirst, you need to [install Docker](https://www.docker.com/get-started).\nThen run the following command\n(it will take some time, as it completely builds the\nenvironment needed to run AllenNLP.)\n\n```bash\ndocker build -f Dockerfile.pip --tag allennlp/allennlp:latest .\n```\n\nYou should now be able to see this image listed by running `docker images allennlp`.\n\n```\nREPOSITORY          TAG                 IMAGE ID            CREATED             SIZE\nallennlp/allennlp            latest              b66aee6cb593        5 minutes ago       2.38GB\n```\n\n### Running the Docker image\n\nYou 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.\n\nYou can test your installation by running  `allennlp test-install`.\n\n## Issues\n\nEveryone 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.\n\n## Contributions\n\nThe 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.\n\nPull 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.\n\n## Citing\n\nIf 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).\n\n```\n@inproceedings{Gardner2017AllenNLP,\n  title={AllenNLP: A Deep Semantic Natural Language Processing Platform},\n  author={Matt Gardner and Joel Grus and Mark Neumann and Oyvind Tafjord\n    and Pradeep Dasigi and Nelson F. Liu and Matthew Peters and\n    Michael Schmitz and Luke S. Zettlemoyer},\n  year={2017},\n  Eprint = {arXiv:1803.07640},\n}\n```\n\n## Team\n\nAllenNLP is an open-source project backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/).\nAI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.\nTo learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/allennlp/graphs/contributors) page.\n\n\n",
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