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|
%global _empty_manifest_terminate_build 0
Name: python-textacy
Version: 0.13.0
Release: 1
Summary: NLP, before and after spaCy
License: Copyright 2016 Chartbeat, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
URL: https://pypi.org/project/textacy/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/04/fe/4a578d9f68e7aaf6b7be7d8df974ab3b1b21f2e64d492919adda3cd80b71/textacy-0.13.0.tar.gz
BuildArch: noarch
Requires: python3-cachetools
Requires: python3-catalogue
Requires: python3-cytoolz
Requires: python3-floret
Requires: python3-jellyfish
Requires: python3-joblib
Requires: python3-networkx
Requires: python3-numpy
Requires: python3-pyphen
Requires: python3-requests
Requires: python3-scipy
Requires: python3-scikit-learn
Requires: python3-spacy
Requires: python3-tqdm
Requires: python3-black
Requires: python3-isort
Requires: python3-mypy
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-ruff
Requires: python3-black
Requires: python3-build
Requires: python3-isort
Requires: python3-mypy
Requires: python3-recommonmark
Requires: python3-sphinx
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-ruff
Requires: python3-twine
Requires: python3-wheel
Requires: python3-Jinja2
Requires: python3-recommonmark
Requires: python3-sphinx
Requires: python3-matplotlib
%description
## textacy: NLP, before and after spaCy
`textacy` is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, `textacy` focuses primarily on the tasks that come before and follow after.
[](https://travis-ci.org/chartbeat-labs/textacy)
[](https://github.com/chartbeat-labs/textacy/releases)
[](https://pypi.python.org/pypi/textacy)
[](https://anaconda.org/conda-forge/textacy)
### features
- Access and extend spaCy's core functionality for working with one or many documents through convenient methods and custom extensions
- Load prepared datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
- Clean, normalize, and explore raw text before processing it with spaCy
- Extract structured information from processed documents, including n-grams, entities, acronyms, keyterms, and SVO triples
- Compare strings and sequences using a variety of similarity metrics
- Tokenize and vectorize documents then train, interpret, and visualize topic models
- Compute text readability and lexical diversity statistics, including Flesch-Kincaid grade level, multilingual Flesch Reading Ease, and Type-Token Ratio
... *and much more!*
### links
- Download: https://pypi.org/project/textacy
- Documentation: https://textacy.readthedocs.io
- Source code: https://github.com/chartbeat-labs/textacy
- Bug Tracker: https://github.com/chartbeat-labs/textacy/issues
### maintainer
Howdy, y'all. 👋
- Burton DeWilde (<burtdewilde@gmail.com>)
%package -n python3-textacy
Summary: NLP, before and after spaCy
Provides: python-textacy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-textacy
## textacy: NLP, before and after spaCy
`textacy` is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, `textacy` focuses primarily on the tasks that come before and follow after.
[](https://travis-ci.org/chartbeat-labs/textacy)
[](https://github.com/chartbeat-labs/textacy/releases)
[](https://pypi.python.org/pypi/textacy)
[](https://anaconda.org/conda-forge/textacy)
### features
- Access and extend spaCy's core functionality for working with one or many documents through convenient methods and custom extensions
- Load prepared datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
- Clean, normalize, and explore raw text before processing it with spaCy
- Extract structured information from processed documents, including n-grams, entities, acronyms, keyterms, and SVO triples
- Compare strings and sequences using a variety of similarity metrics
- Tokenize and vectorize documents then train, interpret, and visualize topic models
- Compute text readability and lexical diversity statistics, including Flesch-Kincaid grade level, multilingual Flesch Reading Ease, and Type-Token Ratio
... *and much more!*
### links
- Download: https://pypi.org/project/textacy
- Documentation: https://textacy.readthedocs.io
- Source code: https://github.com/chartbeat-labs/textacy
- Bug Tracker: https://github.com/chartbeat-labs/textacy/issues
### maintainer
Howdy, y'all. 👋
- Burton DeWilde (<burtdewilde@gmail.com>)
%package help
Summary: Development documents and examples for textacy
Provides: python3-textacy-doc
%description help
## textacy: NLP, before and after spaCy
`textacy` is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, `textacy` focuses primarily on the tasks that come before and follow after.
[](https://travis-ci.org/chartbeat-labs/textacy)
[](https://github.com/chartbeat-labs/textacy/releases)
[](https://pypi.python.org/pypi/textacy)
[](https://anaconda.org/conda-forge/textacy)
### features
- Access and extend spaCy's core functionality for working with one or many documents through convenient methods and custom extensions
- Load prepared datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
- Clean, normalize, and explore raw text before processing it with spaCy
- Extract structured information from processed documents, including n-grams, entities, acronyms, keyterms, and SVO triples
- Compare strings and sequences using a variety of similarity metrics
- Tokenize and vectorize documents then train, interpret, and visualize topic models
- Compute text readability and lexical diversity statistics, including Flesch-Kincaid grade level, multilingual Flesch Reading Ease, and Type-Token Ratio
... *and much more!*
### links
- Download: https://pypi.org/project/textacy
- Documentation: https://textacy.readthedocs.io
- Source code: https://github.com/chartbeat-labs/textacy
- Bug Tracker: https://github.com/chartbeat-labs/textacy/issues
### maintainer
Howdy, y'all. 👋
- Burton DeWilde (<burtdewilde@gmail.com>)
%prep
%autosetup -n textacy-0.13.0
%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-textacy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.13.0-1
- Package Spec generated
|
