Metadata-Version: 2.1
Name: allennlp
Version: 0.9.1.dev20200404
Summary: An open-source NLP research library, built on PyTorch.
Home-page: https://github.com/allenai/allennlp
Author: Allen Institute for Artificial Intelligence
Author-email: allennlp@allenai.org
License: Apache
Description: <p align="center"><img width="40%" src="docs/img/allennlp-logo-dark.png" /></p>
        
        [![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)
        [![codecov](https://codecov.io/gh/allenai/allennlp/branch/master/graph/badge.svg)](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](https://allennlp.org/)
        * [Tutorial](https://allennlp.org/tutorials)
        * [Forum](https://discourse.allennlp.org)
        * [Documentation](https://docs.allennlp.org/master/)
        * [Contributing Guidelines](CONTRIBUTING.md)
        * [Pretrained Models](https://github.com/allenai/allennlp-hub/blob/master/allennlp_hub/pretrained/allennlp_pretrained.py)
        * [Continuous Build](http://build.allennlp.org/)
        * [Nightly Releases](https://pypi.org/project/allennlp/#history)
        
        ## 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.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/projects/conda/en/latest/user-guide/install/index.html).
        
        2.  Create a Conda environment with Python 3.7
        
            ```bash
            conda create -n allennlp python=3.7
            ```
        
        3.  Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use AllenNLP.
        
            ```bash
            conda activate allennlp
            ```
        
        #### Installing the library and dependencies
        
        Installing the library and dependencies is simple using `pip`.
        
           ```bash
           pip install allennlp
           ```
        *Looking for bleeding edge features? You can install nightly releases directly from [pypi](https://pypi.org/project/allennlp/#history)*
        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`.
        
        ### 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
           mkdir -p $HOME/.allennlp/
           docker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:v0.9.0
           ```
        
        You can test the Docker environment with `docker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:v0.9.0 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 3.7 virtual environment, and install AllenNLP in `editable` mode by running:
        
          ```bash
          pip install --editable .
          pip install -r dev-requirements.txt
          ```
        
        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`.
        `./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 `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:
          
            elmo         Create word vectors using a pretrained ELMo model.
            evaluate     Evaluate the specified model + dataset.
            find-lr      Find a learning rate range.
            predict      Use a trained model to make predictions.
            print-results
                         Print results from allennlp serialization directories to the
                         console.
            test-install
                         Run the unit tests.
            train        Train a model.
        ```
        
        ## 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`.
        
        ## Issues
        
        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.
        
        ## Contributions
        
        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.
        
        ## 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).
        
        ```bibtex
        @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)](https://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.
        
Keywords: allennlp NLP deep learning machine reading
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6.1
Description-Content-Type: text/markdown
