%global _empty_manifest_terminate_build 0
Name:		python-argument-esa-model
Version:	3.11.112
Release:	1
Summary:	An ESA implementation in python.
License:	MIT License
URL:		https://git.webis.de/args/args-topic-modeling/tree/master/src/python/esa
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/85/c8/4fc090bc1b235a0b08e8e7c5f61ede22688f374d875f24e75f639f776b2d/argument_esa_model-3.11.112.linux-x86_64.tar.gz
BuildArch:	noarch


%description
# Get the required resources
<code> scp -r webis@webislab40.medien.uni-weimar.de:/home/weci2587/projects/args-topic-modeling/resources . </code>

# To run the ESA-script with all terms run:
## For normal ESA:

    ./esa-all-terms.py  --similarity cos
                        --matrix-path <path_to_resources>/resources/esa-plain/<debatepedia|strategic-intelligence|wikipedia>.mat
                        --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                        --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                        --input-path <path_to_input_file>
                        --output-path <path_to_output_file>

## For word2vec-ESA:

    ./esa-all-terms.py  --similarity max
                        --matrix-path <path_to_resources>/resources/esa-w2v/<debatepedia|strategic-intelligence|wikipedia>.mat
                        --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                        --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                        --input-path <path_to_input_file>
                        --output-path <path_to_output_file>

# To run the word2vec-ESA with reduced terms run:

    ./esa-top-n-terms.py    -n <number_of_terms> 
                            --corpus-path <path_to_resources>/resources/corpora/<debatepedia|strategic-intelligence|wikipedia>.csv
                            --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                            --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                            --input-path <path_to_input_file>
                            --output-path <path_to_output_file>

The input document must be a csv file with "|" as the separator and must contain the column "document", which is used as the input text for the ESA.



%package -n python3-argument-esa-model
Summary:	An ESA implementation in python.
Provides:	python-argument-esa-model
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-argument-esa-model
# Get the required resources
<code> scp -r webis@webislab40.medien.uni-weimar.de:/home/weci2587/projects/args-topic-modeling/resources . </code>

# To run the ESA-script with all terms run:
## For normal ESA:

    ./esa-all-terms.py  --similarity cos
                        --matrix-path <path_to_resources>/resources/esa-plain/<debatepedia|strategic-intelligence|wikipedia>.mat
                        --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                        --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                        --input-path <path_to_input_file>
                        --output-path <path_to_output_file>

## For word2vec-ESA:

    ./esa-all-terms.py  --similarity max
                        --matrix-path <path_to_resources>/resources/esa-w2v/<debatepedia|strategic-intelligence|wikipedia>.mat
                        --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                        --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                        --input-path <path_to_input_file>
                        --output-path <path_to_output_file>

# To run the word2vec-ESA with reduced terms run:

    ./esa-top-n-terms.py    -n <number_of_terms> 
                            --corpus-path <path_to_resources>/resources/corpora/<debatepedia|strategic-intelligence|wikipedia>.csv
                            --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                            --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                            --input-path <path_to_input_file>
                            --output-path <path_to_output_file>

The input document must be a csv file with "|" as the separator and must contain the column "document", which is used as the input text for the ESA.



%package help
Summary:	Development documents and examples for argument-esa-model
Provides:	python3-argument-esa-model-doc
%description help
# Get the required resources
<code> scp -r webis@webislab40.medien.uni-weimar.de:/home/weci2587/projects/args-topic-modeling/resources . </code>

# To run the ESA-script with all terms run:
## For normal ESA:

    ./esa-all-terms.py  --similarity cos
                        --matrix-path <path_to_resources>/resources/esa-plain/<debatepedia|strategic-intelligence|wikipedia>.mat
                        --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                        --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                        --input-path <path_to_input_file>
                        --output-path <path_to_output_file>

## For word2vec-ESA:

    ./esa-all-terms.py  --similarity max
                        --matrix-path <path_to_resources>/resources/esa-w2v/<debatepedia|strategic-intelligence|wikipedia>.mat
                        --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                        --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                        --input-path <path_to_input_file>
                        --output-path <path_to_output_file>

# To run the word2vec-ESA with reduced terms run:

    ./esa-top-n-terms.py    -n <number_of_terms> 
                            --corpus-path <path_to_resources>/resources/corpora/<debatepedia|strategic-intelligence|wikipedia>.csv
                            --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                            --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                            --input-path <path_to_input_file>
                            --output-path <path_to_output_file>

The input document must be a csv file with "|" as the separator and must contain the column "document", which is used as the input text for the ESA.



%prep
%autosetup -n argument-esa-model-3.11.112

%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-argument-esa-model -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 3.11.112-1
- Package Spec generated