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diff --git a/python-nagisa.spec b/python-nagisa.spec new file mode 100644 index 0000000..30327c3 --- /dev/null +++ b/python-nagisa.spec @@ -0,0 +1,128 @@ +%global _empty_manifest_terminate_build 0 +Name: python-nagisa +Version: 0.2.8 +Release: 1 +Summary: A Japanese tokenizer based on recurrent neural networks +License: MIT License +URL: https://github.com/taishi-i/nagisa +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/af/81/2c61ffc4c532efc41e9fdd95109c3d844ff0627d212d95adee3744faa6dc/nagisa-0.2.8.tar.gz + +Requires: python3-six +Requires: python3-numpy +Requires: python3-DyNet + +%description +[](https://github.com/taishi-i/nagisa/actions/workflows/python-package.yml) +[](https://app.travis-ci.com/taishi-i/nagisa) +[](https://ci.appveyor.com/project/taishi-i/nagisa) +[](https://coveralls.io/github/taishi-i/nagisa?branch=master) +[](https://nagisa.readthedocs.io/en/latest/?badge=latest) +[](https://pypi.python.org/pypi/nagisa) +[](https://pepy.tech/project/nagisa) +Nagisa is a python module for Japanese word segmentation/POS-tagging. +It is designed to be a simple and easy-to-use tool. +This tool has the following features. +- Based on recurrent neural networks. +- The word segmentation model uses character- and word-level features [[池田+]](http://www.anlp.jp/proceedings/annual_meeting/2017/pdf_dir/B6-2.pdf). +- The POS-tagging model uses tag dictionary information [[Inoue+]](http://www.aclweb.org/anthology/K17-1042). +For more details refer to the following links. +- The presentation slide at PyCon JP (2019) is available [here](https://speakerdeck.com/taishii/pycon-jp-2019). +- The article in Japanese is available [here](https://qiita.com/taishi-i/items/5b9275a606b392f7f58e). +- The documentation is available [here](https://nagisa.readthedocs.io/en/latest/?badge=latest). +- The presentation slide at NLP Hacks (2022) is available [here](https://speakerdeck.com/taishii/nlphacks). + +%package -n python3-nagisa +Summary: A Japanese tokenizer based on recurrent neural networks +Provides: python-nagisa +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-nagisa +[](https://github.com/taishi-i/nagisa/actions/workflows/python-package.yml) +[](https://app.travis-ci.com/taishi-i/nagisa) +[](https://ci.appveyor.com/project/taishi-i/nagisa) +[](https://coveralls.io/github/taishi-i/nagisa?branch=master) +[](https://nagisa.readthedocs.io/en/latest/?badge=latest) +[](https://pypi.python.org/pypi/nagisa) +[](https://pepy.tech/project/nagisa) +Nagisa is a python module for Japanese word segmentation/POS-tagging. +It is designed to be a simple and easy-to-use tool. +This tool has the following features. +- Based on recurrent neural networks. +- The word segmentation model uses character- and word-level features [[池田+]](http://www.anlp.jp/proceedings/annual_meeting/2017/pdf_dir/B6-2.pdf). +- The POS-tagging model uses tag dictionary information [[Inoue+]](http://www.aclweb.org/anthology/K17-1042). +For more details refer to the following links. +- The presentation slide at PyCon JP (2019) is available [here](https://speakerdeck.com/taishii/pycon-jp-2019). +- The article in Japanese is available [here](https://qiita.com/taishi-i/items/5b9275a606b392f7f58e). +- The documentation is available [here](https://nagisa.readthedocs.io/en/latest/?badge=latest). +- The presentation slide at NLP Hacks (2022) is available [here](https://speakerdeck.com/taishii/nlphacks). + +%package help +Summary: Development documents and examples for nagisa +Provides: python3-nagisa-doc +%description help +[](https://github.com/taishi-i/nagisa/actions/workflows/python-package.yml) +[](https://app.travis-ci.com/taishi-i/nagisa) +[](https://ci.appveyor.com/project/taishi-i/nagisa) +[](https://coveralls.io/github/taishi-i/nagisa?branch=master) +[](https://nagisa.readthedocs.io/en/latest/?badge=latest) +[](https://pypi.python.org/pypi/nagisa) +[](https://pepy.tech/project/nagisa) +Nagisa is a python module for Japanese word segmentation/POS-tagging. +It is designed to be a simple and easy-to-use tool. +This tool has the following features. +- Based on recurrent neural networks. +- The word segmentation model uses character- and word-level features [[池田+]](http://www.anlp.jp/proceedings/annual_meeting/2017/pdf_dir/B6-2.pdf). +- The POS-tagging model uses tag dictionary information [[Inoue+]](http://www.aclweb.org/anthology/K17-1042). +For more details refer to the following links. +- The presentation slide at PyCon JP (2019) is available [here](https://speakerdeck.com/taishii/pycon-jp-2019). +- The article in Japanese is available [here](https://qiita.com/taishi-i/items/5b9275a606b392f7f58e). +- The documentation is available [here](https://nagisa.readthedocs.io/en/latest/?badge=latest). +- The presentation slide at NLP Hacks (2022) is available [here](https://speakerdeck.com/taishii/nlphacks). + +%prep +%autosetup -n nagisa-0.2.8 + +%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-nagisa -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.8-1 +- Package Spec generated |