%global _empty_manifest_terminate_build 0
Name:		python-top2vec
Version:	1.0.29
Release:	1
Summary:	Top2Vec learns jointly embedded topic, document and word vectors.
License:	BSD
URL:		https://github.com/ddangelov/Top2Vec
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/55/92/8ab9d43de0437c6ec1bf8dcd2f0da484b4c78ed18c4dd189c52e5a26e0a1/top2vec-1.0.29.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-pandas
Requires:	python3-scikit-learn
Requires:	python3-gensim
Requires:	python3-umap-learn
Requires:	python3-hdbscan
Requires:	python3-wordcloud
Requires:	python3-hnswlib
Requires:	python3-tensorflow
Requires:	python3-tensorflow-hub
Requires:	python3-tensorflow-text
Requires:	python3-torch
Requires:	python3-sentence-transformers

%description
Top2Vec is an algorithm for **topic modeling** and **semantic search**. It automatically detects topics present in text
and generates jointly embedded topic, document and word vectors. Once you train the Top2Vec model 
you can:
* Get number of detected topics.
* Get topics.
* Get topic sizes. 
* Get hierarchichal topics. 
* Search topics by keywords.
* Search documents by topic.
* Search documents by keywords.
* Find similar words.
* Find similar documents.
* Expose model with [RESTful-Top2Vec](https://github.com/ddangelov/RESTful-Top2Vec)
See the [paper](http://arxiv.org/abs/2008.09470) for more details on how it works.

%package -n python3-top2vec
Summary:	Top2Vec learns jointly embedded topic, document and word vectors.
Provides:	python-top2vec
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-top2vec
Top2Vec is an algorithm for **topic modeling** and **semantic search**. It automatically detects topics present in text
and generates jointly embedded topic, document and word vectors. Once you train the Top2Vec model 
you can:
* Get number of detected topics.
* Get topics.
* Get topic sizes. 
* Get hierarchichal topics. 
* Search topics by keywords.
* Search documents by topic.
* Search documents by keywords.
* Find similar words.
* Find similar documents.
* Expose model with [RESTful-Top2Vec](https://github.com/ddangelov/RESTful-Top2Vec)
See the [paper](http://arxiv.org/abs/2008.09470) for more details on how it works.

%package help
Summary:	Development documents and examples for top2vec
Provides:	python3-top2vec-doc
%description help
Top2Vec is an algorithm for **topic modeling** and **semantic search**. It automatically detects topics present in text
and generates jointly embedded topic, document and word vectors. Once you train the Top2Vec model 
you can:
* Get number of detected topics.
* Get topics.
* Get topic sizes. 
* Get hierarchichal topics. 
* Search topics by keywords.
* Search documents by topic.
* Search documents by keywords.
* Find similar words.
* Find similar documents.
* Expose model with [RESTful-Top2Vec](https://github.com/ddangelov/RESTful-Top2Vec)
See the [paper](http://arxiv.org/abs/2008.09470) for more details on how it works.

%prep
%autosetup -n top2vec-1.0.29

%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-top2vec -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.29-1
- Package Spec generated