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%global _empty_manifest_terminate_build 0
Name: python-pyhacrf-datamade
Version: 0.2.6
Release: 1
Summary: Hidden alignment conditional random field, a discriminative string edit distance
License: BSD License
URL: https://github.com/datamade/pyhacrf
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2f/47/d2dea0847a98445d0faac8699f5727a747fb3a9cadb68eb8fbbcc5aa48be/pyhacrf-datamade-0.2.6.tar.gz
Requires: python3-PyLBFGS
Requires: python3-numpy
Requires: python3-numpy
Requires: python3-numpy
Requires: python3-numpy
%description
Hidden alignment conditional random field for classifying string pairs -
a learnable edit distance.
Part of the Dedupe.io cloud service and open source toolset for de-duplicating and finding fuzzy matches in your data: https://dedupe.io
This package aims to implement the HACRF machine learning model with a
``sklearn``-like interface. It includes ways to fit a model to training
examples and score new example.
The model takes string pairs as input and classify them into any number
of classes. In McCallum's original paper the model was applied to the
database deduplication problem. Each database entry was paired with
every other entry and the model then classified whether the pair was a
'match' or a 'mismatch' based on training examples of matches and
mismatches.
I also tried to use it as learnable string edit distance for normalizing
noisy text. See *A Conditional Random Field for Discriminatively-trained
Finite-state String Edit Distance* by McCallum, Bellare, and Pereira,
and the report *Conditional Random Fields for Noisy text normalisation*
by Dirko Coetsee.
%package -n python3-pyhacrf-datamade
Summary: Hidden alignment conditional random field, a discriminative string edit distance
Provides: python-pyhacrf-datamade
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-pyhacrf-datamade
Hidden alignment conditional random field for classifying string pairs -
a learnable edit distance.
Part of the Dedupe.io cloud service and open source toolset for de-duplicating and finding fuzzy matches in your data: https://dedupe.io
This package aims to implement the HACRF machine learning model with a
``sklearn``-like interface. It includes ways to fit a model to training
examples and score new example.
The model takes string pairs as input and classify them into any number
of classes. In McCallum's original paper the model was applied to the
database deduplication problem. Each database entry was paired with
every other entry and the model then classified whether the pair was a
'match' or a 'mismatch' based on training examples of matches and
mismatches.
I also tried to use it as learnable string edit distance for normalizing
noisy text. See *A Conditional Random Field for Discriminatively-trained
Finite-state String Edit Distance* by McCallum, Bellare, and Pereira,
and the report *Conditional Random Fields for Noisy text normalisation*
by Dirko Coetsee.
%package help
Summary: Development documents and examples for pyhacrf-datamade
Provides: python3-pyhacrf-datamade-doc
%description help
Hidden alignment conditional random field for classifying string pairs -
a learnable edit distance.
Part of the Dedupe.io cloud service and open source toolset for de-duplicating and finding fuzzy matches in your data: https://dedupe.io
This package aims to implement the HACRF machine learning model with a
``sklearn``-like interface. It includes ways to fit a model to training
examples and score new example.
The model takes string pairs as input and classify them into any number
of classes. In McCallum's original paper the model was applied to the
database deduplication problem. Each database entry was paired with
every other entry and the model then classified whether the pair was a
'match' or a 'mismatch' based on training examples of matches and
mismatches.
I also tried to use it as learnable string edit distance for normalizing
noisy text. See *A Conditional Random Field for Discriminatively-trained
Finite-state String Edit Distance* by McCallum, Bellare, and Pereira,
and the report *Conditional Random Fields for Noisy text normalisation*
by Dirko Coetsee.
%prep
%autosetup -n pyhacrf-datamade-0.2.6
%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-pyhacrf-datamade -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.6-1
- Package Spec generated
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