Package: unofficial-allennlp-nightly
AnApache 2.0NLP research library, built on PyTorch,
for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.Quick LinksWebsiteTutorialForumDocumentationContributing GuidelinesPretrained ModelsContinuous BuildPackage Overviewallennlpan open-source NLP research library, built on PyTorchallennlp.commandsfunctionality for a CLI and web serviceallennlp.dataa data processing module for loading datasets and encoding strings as integers for representation in matricesallennlp.modelsa collection of state-of-the-art modelsallennlp.modulesa collection of PyTorch modules for use with textallennlp.nntensor utility functions, such as initializers and activation functionsallennlp.trainingfunctionality for training modelsInstallationAllenNLP requires Python 3.6.1 or later. The preferred way to install AllenNLP is viapip.  Just runpip install allennlpin 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 pipSetting up a virtual environmentCondacan 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.Download and install Conda.Create a Conda environment with Python 3.7condacreate-nallennlppython=3.7Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use AllenNLP.condaactivateallennlpInstalling the library and dependenciesInstalling the library and dependencies is simple usingpip.pipinstallallennlpThat'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 typingallennlpinto a terminal.You can now test your installation withallennlp test-install.Installing using DockerDocker 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 haveinstalled Dockerjust run the following command to get an environment that will run on either the cpu or gpu.mkdir-p$HOME/.allennlp/
dockerrun--rm-v$HOME/.allennlp:/root/.allennlpallennlp/allennlp:v0.9.0You can test the Docker environment withdocker run --rm -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:v0.9.0 test-install.Installing from sourceYou can also install AllenNLP by cloning our git repository:gitclonehttps://github.com/allenai/allennlp.gitCreate a Python 3.7 virtual environment, and install AllenNLP ineditablemode by running:pipinstall--editable.
pipinstall-rdev-requirements.txtThis will makeallennlpavailable on your system but it will use the sources from the local clone
you made of the source repository.You can test your installation withallennlp test-install../scripts/verify.pywill run the full suite of tests used by our continuous build environment.Running AllenNLPOnce you've installed AllenNLP, you can run the command-line interface either
with theallennlpcommand (if you installed viapip) orallennlp(if you installed via source).$ allennlp
Run AllenNLP

optional arguments:
  -h, --help     show this help message and exit
  --version      show program's version number and exit

Commands:

    train        Train a model.
    evaluate     Evaluate the specified model + dataset.
    predict      Use a trained model to make predictions.
    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.
    make-vocab   Create a vocabulary, compute dataset statistics and other
                 training utilities.
    test-install
                 Run the unit tests.
    find-lr      Find a learning rate range.
    print-results
                 Print results from allennlp serialization directories to the
                 console.Docker imagesAllenNLP releases Docker images toDocker Hubfor each release.  For information on how to run these releases, seeInstalling using Docker.Building a Docker imageFor 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 toinstall Docker.
Then run the following command (it will take some time, as it completely builds the environment needed to run AllenNLP.)dockerbuild-fDockerfile.pip--tagallennlp/allennlp:latest.You should now be able to see this image listed by runningdocker images allennlp.REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
allennlp/allennlp            latest              b66aee6cb593        5 minutes ago       2.38GBRunning the Docker imageYou can run the image withdocker run --rm -it allennlp/allennlp:latest.  The--rmflag cleans up the image on exit and the-itflags make the session interactive so you can use the bash shell the Docker image starts.You can test your installation by runningallennlp test-install.IssuesEveryone 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.ContributionsThe 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.CitingIf you use AllenNLP in your research, please citeAllenNLP: 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 Tafjordand Pradeep Dasigi and Nelson F. Liu and Matthew Peters andMichael Schmitz and Luke S. Zettlemoyer},year={2017},Eprint={arXiv:1803.07640},}TeamAllenNLP is an open-source project backed bythe 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, seeour contributorspage.