blob: 30327c3656f13aae05cee553a8aa951c41e3df3e (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
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
|
