| 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 |
## 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 3.6
```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
conda 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 https://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
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 3.6 virtual environment, and install AllenNLP in `editable` mode by running:
```bash
pip install --editable .
```
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`.
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 `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 Run the configuration wizard.
train Train a model.
evaluate Evaluate the specified model + dataset.
predict Use a trained model to make predictions.
make-vocab Create a vocabulary.
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.
test-install
Run the unit tests.
find-lr Find a learning rate range.
```
## 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.
%package -n python3-allennlp-pvt-nightly
Summary: An open-source NLP research library, built on PyTorch.
Provides: python-allennlp-pvt-nightly
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-allennlp-pvt-nightly
| 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 |
## 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 3.6
```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
conda 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 https://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
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 3.6 virtual environment, and install AllenNLP in `editable` mode by running:
```bash
pip install --editable .
```
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`.
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 `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 Run the configuration wizard.
train Train a model.
evaluate Evaluate the specified model + dataset.
predict Use a trained model to make predictions.
make-vocab Create a vocabulary.
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.
test-install
Run the unit tests.
find-lr Find a learning rate range.
```
## 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.
%package help
Summary: Development documents and examples for allennlp-pvt-nightly
Provides: python3-allennlp-pvt-nightly-doc
%description help
| 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 |
## 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 3.6
```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
conda 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 https://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
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 3.6 virtual environment, and install AllenNLP in `editable` mode by running:
```bash
pip install --editable .
```
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`.
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 `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 Run the configuration wizard.
train Train a model.
evaluate Evaluate the specified model + dataset.
predict Use a trained model to make predictions.
make-vocab Create a vocabulary.
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.
test-install
Run the unit tests.
find-lr Find a learning rate range.
```
## 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.
%prep
%autosetup -n allennlp-pvt-nightly-0.9.1.dev201910011800
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-allennlp-pvt-nightly -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot 