automatic import of python-wikipedia2vecopeneuler20.03

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+/wikipedia2vec-1.0.5.tar.gz
diff --git a/python-wikipedia2vec.spec b/python-wikipedia2vec.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-wikipedia2vec
+Version:	1.0.5
+Release:	1
+Summary:	A tool for learning vector representations of words and entities from Wikipedia
+License:	Apache Software License
+URL:		http://wikipedia2vec.github.io/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/89/83/15ab878fe5a93590b80bac8c3a8b0ad5f5dec5d0ea1071f9a17dbce5c33b/wikipedia2vec-1.0.5.tar.gz
+BuildArch:	noarch
+
+
+%description
+[![Fury badge](https://badge.fury.io/py/wikipedia2vec.png)](http://badge.fury.io/py/wikipedia2vec)
+[![CircleCI](https://circleci.com/gh/wikipedia2vec/wikipedia2vec.svg?style=svg)](https://circleci.com/gh/wikipedia2vec/wikipedia2vec)
+Wikipedia2Vec is a tool used for obtaining embeddings (or vector representations) of words and entities (i.e., concepts that have corresponding pages in Wikipedia) from Wikipedia.
+It is developed and maintained by [Studio Ousia](http://www.ousia.jp).
+This tool enables you to learn embeddings of words and entities simultaneously, and places similar words and entities close to one another in a continuous vector space.
+Embeddings can be easily trained by a single command with a publicly available Wikipedia dump as input.
+This tool implements the [conventional skip-gram model](https://en.wikipedia.org/wiki/Word2vec) to learn the embeddings of words, and its extension proposed in [Yamada et al. (2016)](https://arxiv.org/abs/1601.01343) to learn the embeddings of entities.
+An empirical comparison between Wikipedia2Vec and existing embedding tools (i.e., FastText, Gensim, RDF2Vec, and Wiki2vec) is available [here](https://arxiv.org/abs/1812.06280).
+Documentation  are available online at [http://wikipedia2vec.github.io/](http://wikipedia2vec.github.io/).
+## Basic Usage
+Wikipedia2Vec can be installed via PyPI:
+```bash
+% pip install wikipedia2vec
+```
+With this tool, embeddings can be learned by running a *train* command with a Wikipedia dump as input.
+For example, the following commands download the latest English Wikipedia dump and learn embeddings from this dump:
+```bash
+% wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2
+% wikipedia2vec train enwiki-latest-pages-articles.xml.bz2 MODEL_FILE
+```
+Then, the learned embeddings are written to *MODEL\_FILE*.
+Note that this command can take many optional parameters.
+Please refer to [our documentation](https://wikipedia2vec.github.io/wikipedia2vec/commands/) for further details.
+## Pretrained Embeddings
+Pretrained embeddings for 12 languages (i.e., English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Polish, Portuguese, Russian, and Spanish) can be downloaded from [this page](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
+## Use Cases
+Wikipedia2Vec has been applied to the following tasks:
+* Entity linking: [Yamada et al., 2016](https://arxiv.org/abs/1601.01343), [Eshel et al., 2017](https://arxiv.org/abs/1706.09147), [Chen et al., 2019](https://arxiv.org/abs/1911.03834), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681), [van Hulst et al., 2020](https://arxiv.org/abs/2006.01969).
+* Named entity recognition: [Sato et al., 2017](http://www.aclweb.org/anthology/I17-2017), [Lara-Clares and Garcia-Serrano, 2019](http://ceur-ws.org/Vol-2421/eHealth-KD_paper_6.pdf).
+* Question answering: [Yamada et al., 2017](https://arxiv.org/abs/1803.08652), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
+* Entity typing: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960).
+* Text classification: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960), [Yamada and Shindo, 2019](https://arxiv.org/abs/1909.01259), [Alam et al., 2020](https://link.springer.com/chapter/10.1007/978-3-030-61244-3_9).
+* Relation classification: [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
+* Paraphrase detection: [Duong et al., 2018](https://ieeexplore.ieee.org/abstract/document/8606845).
+* Knowledge graph completion: [Shah et al., 2019](https://aaai.org/ojs/index.php/AAAI/article/view/4162), [Shah et al., 2020](https://www.aclweb.org/anthology/2020.textgraphs-1.9/).
+* Fake news detection: [Singh et al., 2019](https://arxiv.org/abs/1906.11126), [Ghosal et al., 2020](https://arxiv.org/abs/2010.10836).
+* Plot analysis of movies: [Papalampidi et al., 2019](https://arxiv.org/abs/1908.10328).
+* Novel entity discovery: [Zhang et al., 2020](https://arxiv.org/abs/2002.00206).
+* Entity retrieval: [Gerritse et al., 2020](https://link.springer.com/chapter/10.1007%2F978-3-030-45439-5_7).
+* Deepfake detection: [Zhong et al., 2020](https://arxiv.org/abs/2010.07475).
+* Conversational information seeking: [Rodriguez et al., 2020](https://arxiv.org/abs/2005.00172).
+* Query expansion: [Rosin et al., 2020](https://arxiv.org/abs/2012.12065).
+## References
+If you use Wikipedia2Vec in a scientific publication, please cite the following paper:
+Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto, [Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia](https://arxiv.org/abs/1812.06280).
+```
+@inproceedings{yamada2020wikipedia2vec,
+  title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
+  author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
+  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
+  year = {2020},
+  publisher = {Association for Computational Linguistics},
+  pages = {23--30}
+}
+```
+The embedding model was originally proposed in the following paper:
+Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://arxiv.org/abs/1601.01343).
+```
+@inproceedings{yamada2016joint,
+  title={Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation},
+  author={Yamada, Ikuya and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
+  booktitle={Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning},
+  year={2016},
+  publisher={Association for Computational Linguistics},
+  pages={250--259}
+}
+```
+The text classification model implemented in [this example](https://github.com/wikipedia2vec/wikipedia2vec/tree/master/examples/text_classification) was proposed in the following paper:
+Ikuya Yamada, Hiroyuki Shindo, [Neural Attentive Bag-of-Entities Model for Text Classification](https://arxiv.org/abs/1909.01259).
+```
+@article{yamada2019neural,
+  title={Neural Attentive Bag-of-Entities Model for Text Classification},
+  author={Yamada, Ikuya and Shindo, Hiroyuki},
+  booktitle={Proceedings of The 23th SIGNLL Conference on Computational Natural Language Learning},
+  year={2019},
+  publisher={Association for Computational Linguistics},
+  pages = {563--573}
+}
+```
+## License
+[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)
+
+%package -n python3-wikipedia2vec
+Summary:	A tool for learning vector representations of words and entities from Wikipedia
+Provides:	python-wikipedia2vec
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-wikipedia2vec
+[![Fury badge](https://badge.fury.io/py/wikipedia2vec.png)](http://badge.fury.io/py/wikipedia2vec)
+[![CircleCI](https://circleci.com/gh/wikipedia2vec/wikipedia2vec.svg?style=svg)](https://circleci.com/gh/wikipedia2vec/wikipedia2vec)
+Wikipedia2Vec is a tool used for obtaining embeddings (or vector representations) of words and entities (i.e., concepts that have corresponding pages in Wikipedia) from Wikipedia.
+It is developed and maintained by [Studio Ousia](http://www.ousia.jp).
+This tool enables you to learn embeddings of words and entities simultaneously, and places similar words and entities close to one another in a continuous vector space.
+Embeddings can be easily trained by a single command with a publicly available Wikipedia dump as input.
+This tool implements the [conventional skip-gram model](https://en.wikipedia.org/wiki/Word2vec) to learn the embeddings of words, and its extension proposed in [Yamada et al. (2016)](https://arxiv.org/abs/1601.01343) to learn the embeddings of entities.
+An empirical comparison between Wikipedia2Vec and existing embedding tools (i.e., FastText, Gensim, RDF2Vec, and Wiki2vec) is available [here](https://arxiv.org/abs/1812.06280).
+Documentation  are available online at [http://wikipedia2vec.github.io/](http://wikipedia2vec.github.io/).
+## Basic Usage
+Wikipedia2Vec can be installed via PyPI:
+```bash
+% pip install wikipedia2vec
+```
+With this tool, embeddings can be learned by running a *train* command with a Wikipedia dump as input.
+For example, the following commands download the latest English Wikipedia dump and learn embeddings from this dump:
+```bash
+% wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2
+% wikipedia2vec train enwiki-latest-pages-articles.xml.bz2 MODEL_FILE
+```
+Then, the learned embeddings are written to *MODEL\_FILE*.
+Note that this command can take many optional parameters.
+Please refer to [our documentation](https://wikipedia2vec.github.io/wikipedia2vec/commands/) for further details.
+## Pretrained Embeddings
+Pretrained embeddings for 12 languages (i.e., English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Polish, Portuguese, Russian, and Spanish) can be downloaded from [this page](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
+## Use Cases
+Wikipedia2Vec has been applied to the following tasks:
+* Entity linking: [Yamada et al., 2016](https://arxiv.org/abs/1601.01343), [Eshel et al., 2017](https://arxiv.org/abs/1706.09147), [Chen et al., 2019](https://arxiv.org/abs/1911.03834), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681), [van Hulst et al., 2020](https://arxiv.org/abs/2006.01969).
+* Named entity recognition: [Sato et al., 2017](http://www.aclweb.org/anthology/I17-2017), [Lara-Clares and Garcia-Serrano, 2019](http://ceur-ws.org/Vol-2421/eHealth-KD_paper_6.pdf).
+* Question answering: [Yamada et al., 2017](https://arxiv.org/abs/1803.08652), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
+* Entity typing: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960).
+* Text classification: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960), [Yamada and Shindo, 2019](https://arxiv.org/abs/1909.01259), [Alam et al., 2020](https://link.springer.com/chapter/10.1007/978-3-030-61244-3_9).
+* Relation classification: [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
+* Paraphrase detection: [Duong et al., 2018](https://ieeexplore.ieee.org/abstract/document/8606845).
+* Knowledge graph completion: [Shah et al., 2019](https://aaai.org/ojs/index.php/AAAI/article/view/4162), [Shah et al., 2020](https://www.aclweb.org/anthology/2020.textgraphs-1.9/).
+* Fake news detection: [Singh et al., 2019](https://arxiv.org/abs/1906.11126), [Ghosal et al., 2020](https://arxiv.org/abs/2010.10836).
+* Plot analysis of movies: [Papalampidi et al., 2019](https://arxiv.org/abs/1908.10328).
+* Novel entity discovery: [Zhang et al., 2020](https://arxiv.org/abs/2002.00206).
+* Entity retrieval: [Gerritse et al., 2020](https://link.springer.com/chapter/10.1007%2F978-3-030-45439-5_7).
+* Deepfake detection: [Zhong et al., 2020](https://arxiv.org/abs/2010.07475).
+* Conversational information seeking: [Rodriguez et al., 2020](https://arxiv.org/abs/2005.00172).
+* Query expansion: [Rosin et al., 2020](https://arxiv.org/abs/2012.12065).
+## References
+If you use Wikipedia2Vec in a scientific publication, please cite the following paper:
+Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto, [Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia](https://arxiv.org/abs/1812.06280).
+```
+@inproceedings{yamada2020wikipedia2vec,
+  title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
+  author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
+  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
+  year = {2020},
+  publisher = {Association for Computational Linguistics},
+  pages = {23--30}
+}
+```
+The embedding model was originally proposed in the following paper:
+Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://arxiv.org/abs/1601.01343).
+```
+@inproceedings{yamada2016joint,
+  title={Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation},
+  author={Yamada, Ikuya and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
+  booktitle={Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning},
+  year={2016},
+  publisher={Association for Computational Linguistics},
+  pages={250--259}
+}
+```
+The text classification model implemented in [this example](https://github.com/wikipedia2vec/wikipedia2vec/tree/master/examples/text_classification) was proposed in the following paper:
+Ikuya Yamada, Hiroyuki Shindo, [Neural Attentive Bag-of-Entities Model for Text Classification](https://arxiv.org/abs/1909.01259).
+```
+@article{yamada2019neural,
+  title={Neural Attentive Bag-of-Entities Model for Text Classification},
+  author={Yamada, Ikuya and Shindo, Hiroyuki},
+  booktitle={Proceedings of The 23th SIGNLL Conference on Computational Natural Language Learning},
+  year={2019},
+  publisher={Association for Computational Linguistics},
+  pages = {563--573}
+}
+```
+## License
+[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)
+
+%package help
+Summary:	Development documents and examples for wikipedia2vec
+Provides:	python3-wikipedia2vec-doc
+%description help
+[![Fury badge](https://badge.fury.io/py/wikipedia2vec.png)](http://badge.fury.io/py/wikipedia2vec)
+[![CircleCI](https://circleci.com/gh/wikipedia2vec/wikipedia2vec.svg?style=svg)](https://circleci.com/gh/wikipedia2vec/wikipedia2vec)
+Wikipedia2Vec is a tool used for obtaining embeddings (or vector representations) of words and entities (i.e., concepts that have corresponding pages in Wikipedia) from Wikipedia.
+It is developed and maintained by [Studio Ousia](http://www.ousia.jp).
+This tool enables you to learn embeddings of words and entities simultaneously, and places similar words and entities close to one another in a continuous vector space.
+Embeddings can be easily trained by a single command with a publicly available Wikipedia dump as input.
+This tool implements the [conventional skip-gram model](https://en.wikipedia.org/wiki/Word2vec) to learn the embeddings of words, and its extension proposed in [Yamada et al. (2016)](https://arxiv.org/abs/1601.01343) to learn the embeddings of entities.
+An empirical comparison between Wikipedia2Vec and existing embedding tools (i.e., FastText, Gensim, RDF2Vec, and Wiki2vec) is available [here](https://arxiv.org/abs/1812.06280).
+Documentation  are available online at [http://wikipedia2vec.github.io/](http://wikipedia2vec.github.io/).
+## Basic Usage
+Wikipedia2Vec can be installed via PyPI:
+```bash
+% pip install wikipedia2vec
+```
+With this tool, embeddings can be learned by running a *train* command with a Wikipedia dump as input.
+For example, the following commands download the latest English Wikipedia dump and learn embeddings from this dump:
+```bash
+% wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2
+% wikipedia2vec train enwiki-latest-pages-articles.xml.bz2 MODEL_FILE
+```
+Then, the learned embeddings are written to *MODEL\_FILE*.
+Note that this command can take many optional parameters.
+Please refer to [our documentation](https://wikipedia2vec.github.io/wikipedia2vec/commands/) for further details.
+## Pretrained Embeddings
+Pretrained embeddings for 12 languages (i.e., English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Polish, Portuguese, Russian, and Spanish) can be downloaded from [this page](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
+## Use Cases
+Wikipedia2Vec has been applied to the following tasks:
+* Entity linking: [Yamada et al., 2016](https://arxiv.org/abs/1601.01343), [Eshel et al., 2017](https://arxiv.org/abs/1706.09147), [Chen et al., 2019](https://arxiv.org/abs/1911.03834), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681), [van Hulst et al., 2020](https://arxiv.org/abs/2006.01969).
+* Named entity recognition: [Sato et al., 2017](http://www.aclweb.org/anthology/I17-2017), [Lara-Clares and Garcia-Serrano, 2019](http://ceur-ws.org/Vol-2421/eHealth-KD_paper_6.pdf).
+* Question answering: [Yamada et al., 2017](https://arxiv.org/abs/1803.08652), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
+* Entity typing: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960).
+* Text classification: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960), [Yamada and Shindo, 2019](https://arxiv.org/abs/1909.01259), [Alam et al., 2020](https://link.springer.com/chapter/10.1007/978-3-030-61244-3_9).
+* Relation classification: [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
+* Paraphrase detection: [Duong et al., 2018](https://ieeexplore.ieee.org/abstract/document/8606845).
+* Knowledge graph completion: [Shah et al., 2019](https://aaai.org/ojs/index.php/AAAI/article/view/4162), [Shah et al., 2020](https://www.aclweb.org/anthology/2020.textgraphs-1.9/).
+* Fake news detection: [Singh et al., 2019](https://arxiv.org/abs/1906.11126), [Ghosal et al., 2020](https://arxiv.org/abs/2010.10836).
+* Plot analysis of movies: [Papalampidi et al., 2019](https://arxiv.org/abs/1908.10328).
+* Novel entity discovery: [Zhang et al., 2020](https://arxiv.org/abs/2002.00206).
+* Entity retrieval: [Gerritse et al., 2020](https://link.springer.com/chapter/10.1007%2F978-3-030-45439-5_7).
+* Deepfake detection: [Zhong et al., 2020](https://arxiv.org/abs/2010.07475).
+* Conversational information seeking: [Rodriguez et al., 2020](https://arxiv.org/abs/2005.00172).
+* Query expansion: [Rosin et al., 2020](https://arxiv.org/abs/2012.12065).
+## References
+If you use Wikipedia2Vec in a scientific publication, please cite the following paper:
+Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto, [Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia](https://arxiv.org/abs/1812.06280).
+```
+@inproceedings{yamada2020wikipedia2vec,
+  title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
+  author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
+  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
+  year = {2020},
+  publisher = {Association for Computational Linguistics},
+  pages = {23--30}
+}
+```
+The embedding model was originally proposed in the following paper:
+Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://arxiv.org/abs/1601.01343).
+```
+@inproceedings{yamada2016joint,
+  title={Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation},
+  author={Yamada, Ikuya and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
+  booktitle={Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning},
+  year={2016},
+  publisher={Association for Computational Linguistics},
+  pages={250--259}
+}
+```
+The text classification model implemented in [this example](https://github.com/wikipedia2vec/wikipedia2vec/tree/master/examples/text_classification) was proposed in the following paper:
+Ikuya Yamada, Hiroyuki Shindo, [Neural Attentive Bag-of-Entities Model for Text Classification](https://arxiv.org/abs/1909.01259).
+```
+@article{yamada2019neural,
+  title={Neural Attentive Bag-of-Entities Model for Text Classification},
+  author={Yamada, Ikuya and Shindo, Hiroyuki},
+  booktitle={Proceedings of The 23th SIGNLL Conference on Computational Natural Language Learning},
+  year={2019},
+  publisher={Association for Computational Linguistics},
+  pages = {563--573}
+}
+```
+## License
+[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)
+
+%prep
+%autosetup -n wikipedia2vec-1.0.5
+
+%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-wikipedia2vec -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.5-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..0fcfc28
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+c007ec61d374d69b04ce89f9ae006c76  wikipedia2vec-1.0.5.tar.gz