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diff --git a/python-fugashi.spec b/python-fugashi.spec new file mode 100644 index 0000000..c44ca56 --- /dev/null +++ b/python-fugashi.spec @@ -0,0 +1,472 @@ +%global _empty_manifest_terminate_build 0 +Name: python-fugashi +Version: 1.2.1 +Release: 1 +Summary: A Cython MeCab wrapper for fast, pythonic Japanese tokenization. +License: MIT +URL: https://github.com/polm/fugashi +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4d/aa/008562fae5099633dfe87b68627f2a532b4f92f5348f75edaeec25c990f4/fugashi-1.2.1.tar.gz + +Requires: python3-unidic +Requires: python3-unidic-lite + +%description +[](https://share.streamlit.io/polm/fugashi-streamlit-demo/main/demo.py)
+[](https://pypi.org/project/fugashi/)
+
+[](https://pypi.org/project/fugashi/)
+
+
+# fugashi
+
+<img src="https://github.com/polm/fugashi/raw/master/fugashi.png" width=125 height=125 alt="fugashi by Irasutoya" />
+
+fugashi is a Cython wrapper for [MeCab](https://taku910.github.io/mecab/), a
+Japanese tokenizer and morphological analysis tool. Wheels are provided for
+Linux, OSX, and Win64, and UniDic is [easy to install](#installing-a-dictionary).
+
+**issueを英語で書く必要はありません。**
+
+Check out the [interactive demo][], see the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background
+on why fugashi exists and some of the design decisions, or see [this
+guide][guide] for a basic introduction to Japanese tokenization.
+
+[guide]: https://www.dampfkraft.com/nlp/how-to-tokenize-japanese.html
+[interactive demo]: https://share.streamlit.io/polm/fugashi-streamlit-demo/main/demo.py
+
+If you are on an unsupported platform (like PowerPC), you'll need to install
+MeCab first. It's recommended you install [from
+source](https://github.com/taku910/mecab). If you need to build from source on
+Windows, [@chezou's fork](https://github.com/chezou/mecab) is recommended; see
+[issue #44](https://github.com/polm/fugashi/issues/44#issuecomment-954426115)
+for an explanation of the problems with the official repo.
+
+## Usage
+
+```python
+from fugashi import Tagger
+
+tagger = Tagger('-Owakati')
+text = "麩菓子は、麩を主材料とした日本の菓子。"
+tagger.parse(text)
+# => '麩 菓子 は 、 麩 を 主材 料 と し た 日本 の 菓子 。'
+for word in tagger(text):
+ print(word, word.feature.lemma, word.pos, sep='\t')
+ # "feature" is the Unidic feature data as a named tuple
+```
+
+## Installing a Dictionary
+
+fugashi requires a dictionary. [UniDic](https://unidic.ninjal.ac.jp/) is
+recommended, and two easy-to-install versions are provided.
+
+ - [unidic-lite](https://github.com/polm/unidic-lite), a slightly modified version 2.1.2 of Unidic (from 2013) that's relatively small
+ - [unidic](https://github.com/polm/unidic-py), the latest UniDic 3.1.0, which is 770MB on disk and requires a separate download step
+
+If you just want to make sure things work you can start with `unidic-lite`, but
+for more serious processing `unidic` is recommended. For production use you'll
+generally want to generate your own dictionary too; for details see the [MeCab
+documentation](https://taku910.github.io/mecab/learn.html).
+
+To get either of these dictionaries, you can install them directly using `pip`
+or do the below:
+
+```sh
+pip install fugashi[unidic-lite]
+
+# The full version of UniDic requires a separate download step
+pip install fugashi[unidic]
+python -m unidic download
+```
+
+For more information on the different MeCab dictionaries available, see [this article](https://www.dampfkraft.com/nlp/japanese-tokenizer-dictionaries.html).
+
+## Dictionary Use
+
+fugashi is written with the assumption you'll use Unidic to process Japanese,
+but it supports arbitrary dictionaries.
+
+If you're using a dictionary besides Unidic you can use the GenericTagger like this:
+
+```python
+from fugashi import GenericTagger
+tagger = GenericTagger()
+
+# parse can be used as normal
+tagger.parse('something')
+# features from the dictionary can be accessed by field numbers
+for word in tagger(text):
+ print(word.surface, word.feature[0])
+```
+
+You can also create a dictionary wrapper to get feature information as a named tuple.
+
+```python
+from fugashi import GenericTagger, create_feature_wrapper
+CustomFeatures = create_feature_wrapper('CustomFeatures', 'alpha beta gamma')
+tagger = GenericTagger(wrapper=CustomFeatures)
+for word in tagger.parseToNodeList(text):
+ print(word.surface, word.feature.alpha)
+```
+
+## Citation
+
+If you use fugashi in research, it would be appreciated if you cite this paper. You can read it at [the ACL Anthology](https://www.aclweb.org/anthology/2020.nlposs-1.7/) or [on Arxiv](https://arxiv.org/abs/2010.06858).
+
+ @inproceedings{mccann-2020-fugashi,
+ title = "fugashi, a Tool for Tokenizing {J}apanese in Python",
+ author = "McCann, Paul",
+ booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
+ month = nov,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.nlposs-1.7",
+ pages = "44--51",
+ abstract = "Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.",
+ }
+
+## Alternatives
+
+If you have a problem with fugashi feel free to open an issue. However, there
+are some cases where it might be better to use a different library.
+
+- If you don't want to deal with installing MeCab at all, try [SudachiPy](https://github.com/WorksApplications/sudachi.rs).
+- If you need to work with Korean, try [pymecab-ko](https://github.com/NoUnique/pymecab-ko) or [KoNLPy](https://konlpy.org/en/latest/).
+
+## License and Copyright Notice
+
+fugashi is released under the terms of the [MIT license](./LICENSE). Please
+copy it far and wide.
+
+fugashi is a wrapper for MeCab, and fugashi wheels include MeCab binaries.
+MeCab is copyrighted free software by Taku Kudo `<taku@chasen.org>` and Nippon
+Telegraph and Telephone Corporation, and is redistributed under the [BSD
+License](./LICENSE.mecab).
+ + +%package -n python3-fugashi +Summary: A Cython MeCab wrapper for fast, pythonic Japanese tokenization. +Provides: python-fugashi +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-fugashi +[](https://share.streamlit.io/polm/fugashi-streamlit-demo/main/demo.py)
+[](https://pypi.org/project/fugashi/)
+
+[](https://pypi.org/project/fugashi/)
+
+
+# fugashi
+
+<img src="https://github.com/polm/fugashi/raw/master/fugashi.png" width=125 height=125 alt="fugashi by Irasutoya" />
+
+fugashi is a Cython wrapper for [MeCab](https://taku910.github.io/mecab/), a
+Japanese tokenizer and morphological analysis tool. Wheels are provided for
+Linux, OSX, and Win64, and UniDic is [easy to install](#installing-a-dictionary).
+
+**issueを英語で書く必要はありません。**
+
+Check out the [interactive demo][], see the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background
+on why fugashi exists and some of the design decisions, or see [this
+guide][guide] for a basic introduction to Japanese tokenization.
+
+[guide]: https://www.dampfkraft.com/nlp/how-to-tokenize-japanese.html
+[interactive demo]: https://share.streamlit.io/polm/fugashi-streamlit-demo/main/demo.py
+
+If you are on an unsupported platform (like PowerPC), you'll need to install
+MeCab first. It's recommended you install [from
+source](https://github.com/taku910/mecab). If you need to build from source on
+Windows, [@chezou's fork](https://github.com/chezou/mecab) is recommended; see
+[issue #44](https://github.com/polm/fugashi/issues/44#issuecomment-954426115)
+for an explanation of the problems with the official repo.
+
+## Usage
+
+```python
+from fugashi import Tagger
+
+tagger = Tagger('-Owakati')
+text = "麩菓子は、麩を主材料とした日本の菓子。"
+tagger.parse(text)
+# => '麩 菓子 は 、 麩 を 主材 料 と し た 日本 の 菓子 。'
+for word in tagger(text):
+ print(word, word.feature.lemma, word.pos, sep='\t')
+ # "feature" is the Unidic feature data as a named tuple
+```
+
+## Installing a Dictionary
+
+fugashi requires a dictionary. [UniDic](https://unidic.ninjal.ac.jp/) is
+recommended, and two easy-to-install versions are provided.
+
+ - [unidic-lite](https://github.com/polm/unidic-lite), a slightly modified version 2.1.2 of Unidic (from 2013) that's relatively small
+ - [unidic](https://github.com/polm/unidic-py), the latest UniDic 3.1.0, which is 770MB on disk and requires a separate download step
+
+If you just want to make sure things work you can start with `unidic-lite`, but
+for more serious processing `unidic` is recommended. For production use you'll
+generally want to generate your own dictionary too; for details see the [MeCab
+documentation](https://taku910.github.io/mecab/learn.html).
+
+To get either of these dictionaries, you can install them directly using `pip`
+or do the below:
+
+```sh
+pip install fugashi[unidic-lite]
+
+# The full version of UniDic requires a separate download step
+pip install fugashi[unidic]
+python -m unidic download
+```
+
+For more information on the different MeCab dictionaries available, see [this article](https://www.dampfkraft.com/nlp/japanese-tokenizer-dictionaries.html).
+
+## Dictionary Use
+
+fugashi is written with the assumption you'll use Unidic to process Japanese,
+but it supports arbitrary dictionaries.
+
+If you're using a dictionary besides Unidic you can use the GenericTagger like this:
+
+```python
+from fugashi import GenericTagger
+tagger = GenericTagger()
+
+# parse can be used as normal
+tagger.parse('something')
+# features from the dictionary can be accessed by field numbers
+for word in tagger(text):
+ print(word.surface, word.feature[0])
+```
+
+You can also create a dictionary wrapper to get feature information as a named tuple.
+
+```python
+from fugashi import GenericTagger, create_feature_wrapper
+CustomFeatures = create_feature_wrapper('CustomFeatures', 'alpha beta gamma')
+tagger = GenericTagger(wrapper=CustomFeatures)
+for word in tagger.parseToNodeList(text):
+ print(word.surface, word.feature.alpha)
+```
+
+## Citation
+
+If you use fugashi in research, it would be appreciated if you cite this paper. You can read it at [the ACL Anthology](https://www.aclweb.org/anthology/2020.nlposs-1.7/) or [on Arxiv](https://arxiv.org/abs/2010.06858).
+
+ @inproceedings{mccann-2020-fugashi,
+ title = "fugashi, a Tool for Tokenizing {J}apanese in Python",
+ author = "McCann, Paul",
+ booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
+ month = nov,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.nlposs-1.7",
+ pages = "44--51",
+ abstract = "Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.",
+ }
+
+## Alternatives
+
+If you have a problem with fugashi feel free to open an issue. However, there
+are some cases where it might be better to use a different library.
+
+- If you don't want to deal with installing MeCab at all, try [SudachiPy](https://github.com/WorksApplications/sudachi.rs).
+- If you need to work with Korean, try [pymecab-ko](https://github.com/NoUnique/pymecab-ko) or [KoNLPy](https://konlpy.org/en/latest/).
+
+## License and Copyright Notice
+
+fugashi is released under the terms of the [MIT license](./LICENSE). Please
+copy it far and wide.
+
+fugashi is a wrapper for MeCab, and fugashi wheels include MeCab binaries.
+MeCab is copyrighted free software by Taku Kudo `<taku@chasen.org>` and Nippon
+Telegraph and Telephone Corporation, and is redistributed under the [BSD
+License](./LICENSE.mecab).
+ + +%package help +Summary: Development documents and examples for fugashi +Provides: python3-fugashi-doc +%description help +[](https://share.streamlit.io/polm/fugashi-streamlit-demo/main/demo.py)
+[](https://pypi.org/project/fugashi/)
+
+[](https://pypi.org/project/fugashi/)
+
+
+# fugashi
+
+<img src="https://github.com/polm/fugashi/raw/master/fugashi.png" width=125 height=125 alt="fugashi by Irasutoya" />
+
+fugashi is a Cython wrapper for [MeCab](https://taku910.github.io/mecab/), a
+Japanese tokenizer and morphological analysis tool. Wheels are provided for
+Linux, OSX, and Win64, and UniDic is [easy to install](#installing-a-dictionary).
+
+**issueを英語で書く必要はありません。**
+
+Check out the [interactive demo][], see the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background
+on why fugashi exists and some of the design decisions, or see [this
+guide][guide] for a basic introduction to Japanese tokenization.
+
+[guide]: https://www.dampfkraft.com/nlp/how-to-tokenize-japanese.html
+[interactive demo]: https://share.streamlit.io/polm/fugashi-streamlit-demo/main/demo.py
+
+If you are on an unsupported platform (like PowerPC), you'll need to install
+MeCab first. It's recommended you install [from
+source](https://github.com/taku910/mecab). If you need to build from source on
+Windows, [@chezou's fork](https://github.com/chezou/mecab) is recommended; see
+[issue #44](https://github.com/polm/fugashi/issues/44#issuecomment-954426115)
+for an explanation of the problems with the official repo.
+
+## Usage
+
+```python
+from fugashi import Tagger
+
+tagger = Tagger('-Owakati')
+text = "麩菓子は、麩を主材料とした日本の菓子。"
+tagger.parse(text)
+# => '麩 菓子 は 、 麩 を 主材 料 と し た 日本 の 菓子 。'
+for word in tagger(text):
+ print(word, word.feature.lemma, word.pos, sep='\t')
+ # "feature" is the Unidic feature data as a named tuple
+```
+
+## Installing a Dictionary
+
+fugashi requires a dictionary. [UniDic](https://unidic.ninjal.ac.jp/) is
+recommended, and two easy-to-install versions are provided.
+
+ - [unidic-lite](https://github.com/polm/unidic-lite), a slightly modified version 2.1.2 of Unidic (from 2013) that's relatively small
+ - [unidic](https://github.com/polm/unidic-py), the latest UniDic 3.1.0, which is 770MB on disk and requires a separate download step
+
+If you just want to make sure things work you can start with `unidic-lite`, but
+for more serious processing `unidic` is recommended. For production use you'll
+generally want to generate your own dictionary too; for details see the [MeCab
+documentation](https://taku910.github.io/mecab/learn.html).
+
+To get either of these dictionaries, you can install them directly using `pip`
+or do the below:
+
+```sh
+pip install fugashi[unidic-lite]
+
+# The full version of UniDic requires a separate download step
+pip install fugashi[unidic]
+python -m unidic download
+```
+
+For more information on the different MeCab dictionaries available, see [this article](https://www.dampfkraft.com/nlp/japanese-tokenizer-dictionaries.html).
+
+## Dictionary Use
+
+fugashi is written with the assumption you'll use Unidic to process Japanese,
+but it supports arbitrary dictionaries.
+
+If you're using a dictionary besides Unidic you can use the GenericTagger like this:
+
+```python
+from fugashi import GenericTagger
+tagger = GenericTagger()
+
+# parse can be used as normal
+tagger.parse('something')
+# features from the dictionary can be accessed by field numbers
+for word in tagger(text):
+ print(word.surface, word.feature[0])
+```
+
+You can also create a dictionary wrapper to get feature information as a named tuple.
+
+```python
+from fugashi import GenericTagger, create_feature_wrapper
+CustomFeatures = create_feature_wrapper('CustomFeatures', 'alpha beta gamma')
+tagger = GenericTagger(wrapper=CustomFeatures)
+for word in tagger.parseToNodeList(text):
+ print(word.surface, word.feature.alpha)
+```
+
+## Citation
+
+If you use fugashi in research, it would be appreciated if you cite this paper. You can read it at [the ACL Anthology](https://www.aclweb.org/anthology/2020.nlposs-1.7/) or [on Arxiv](https://arxiv.org/abs/2010.06858).
+
+ @inproceedings{mccann-2020-fugashi,
+ title = "fugashi, a Tool for Tokenizing {J}apanese in Python",
+ author = "McCann, Paul",
+ booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
+ month = nov,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.nlposs-1.7",
+ pages = "44--51",
+ abstract = "Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.",
+ }
+
+## Alternatives
+
+If you have a problem with fugashi feel free to open an issue. However, there
+are some cases where it might be better to use a different library.
+
+- If you don't want to deal with installing MeCab at all, try [SudachiPy](https://github.com/WorksApplications/sudachi.rs).
+- If you need to work with Korean, try [pymecab-ko](https://github.com/NoUnique/pymecab-ko) or [KoNLPy](https://konlpy.org/en/latest/).
+
+## License and Copyright Notice
+
+fugashi is released under the terms of the [MIT license](./LICENSE). Please
+copy it far and wide.
+
+fugashi is a wrapper for MeCab, and fugashi wheels include MeCab binaries.
+MeCab is copyrighted free software by Taku Kudo `<taku@chasen.org>` and Nippon
+Telegraph and Telephone Corporation, and is redistributed under the [BSD
+License](./LICENSE.mecab).
+ + +%prep +%autosetup -n fugashi-1.2.1 + +%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-fugashi -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.1-1 +- Package Spec generated |