%global _empty_manifest_terminate_build 0 Name: python-textpipe Version: 0.12.2 Release: 1 Summary: textpipe: clean and extract metadata from text License: MIT License URL: https://github.com/textpipe/textpipe Source0: https://mirrors.nju.edu.cn/pypi/web/packages/65/b1/5b5544ce361dd1c440f0538b5ee0821a5a3d1c74983bd742d23e720c22ab/textpipe-0.12.2.tar.gz BuildArch: noarch %description # textpipe: clean and extract metadata from text [![Build Status](https://travis-ci.com/textpipe/textpipe.svg?branch=master)](https://travis-ci.com/textpipe/textpipe) ![The textpipe logo](https://avatars3.githubusercontent.com/u/40492530?s=400&u=c65c2c8274cbdcd05b1942d1963d7aa2800e6d7f&v=4) `textpipe` is a Python package for converting raw text in to clean, readable text and extracting metadata from that text. Its functionalities include transforming raw text into readable text by removing HTML tags and extracting metadata such as the number of words and named entities from the text. ## Vision: the zen of textpipe - Designed for use in production pipelines without adult supervision. - Rechargeable batteries included: provide sane defaults and clear examples to adapt. - A uniform interface with thin wrappers around state-of-the-art NLP packages. - As language-agnostic as possible. - Bring your own models. ## Features - Clean raw text by removing `HTML` and other unreadable constructs - Identify the language of text - Extract the number of words, number of sentences, named entities from a text - Calculate the complexity of a text - Obtain text metadata by specifying a pipeline containing all desired elements - Obtain sentiment (polarity and a subjectivity score) - Generates word counts - Computes minhash for cheap similarity estimation of documents ## Installation It is recommended that you install textpipe using a virtual environment. - First, create your virtual environment using [virtualenv](https://virtualenv.pypa.io/en/stable/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/). - Using Venv if your default interpreter is python3.6 ```bash python3 -m venv .venv ``` - Using virtualenv. ```bash virtualenv venv -p python3.6 ``` - Using virtualenvwrapper ```bash mkvirtualenv textpipe -p python3.6 ``` - Install textpipe using pip. ```bash pip install textpipe ``` - Install the required packages using *requirements.txt*. ```bash pip install -r requirements.txt ``` ### A note on spaCy download model requirement While the requirements.txt file that comes with the package calls for spaCy's en_core_web_sm model, this can be changed depending on the model and language you require for your intended use. See [spaCy.io's page on their different models](https://spacy.io/usage/models) for more information. ## Usage example ```python >>> from textpipe import doc, pipeline >>> sample_text = 'Sample text! ' >>> document = doc.Doc(sample_text) >>> print(document.clean) 'Sample text!' >>> print(document.language) 'en' >>> print(document.nwords) 2 >>> pipe = pipeline.Pipeline(['CleanText', 'NWords']) >>> print(pipe(sample_text)) {'CleanText': 'Sample text!', 'NWords': 3} ``` In order to extend the existing Textpipe operations with your own proprietary operations; ```python test_pipe = pipeline.Pipeline(['CleanText', 'NWords']) def custom_op(doc, context=None, settings=None, **kwargs): return 1 custom_argument = {'argument' :1 } test_pipe.register_operation('CUSTOM_STEP', custom_op) test_pipe.steps.append(('CUSTOM_STEP', custom_argument )) ``` ## Contributing See [CONTRIBUTING](CONTRIBUTING.md) for guidelines for contributors. ## Changes 0.12.1 - Bumps redis, tqdm, pyling 0.12.0 - Bumps versions of many dependencies including textacy. Results for keyterm extraction changed. 0.11.9 - Exposes arbitrary SpaCy `ents` properties 0.11.8 - Exposes SpaCy's `cats` attribute 0.11.7 - Bumps spaCy and redis versions 0.11.6 - Fixes bug where gensim model is not cached in pipeline 0.11.5 - Raise TextpipeMissingModelException instead of KeyError 0.11.4 - Bumps spaCy and datasketch dependencies 0.11.1 - Replaces codacy with pylint on CI - Fixes pylint issues 0.11.0 - Adds wrapper around Gensim keyed vectors to construct document embeddings from Redis cache 0.9.0 - Adds functionality to compute document embeddings using a Gensim word2vec model 0.8.6 - Removes non standard utf chars before detecting language 0.8.5 - Bump spaCy to 2.1.3 0.8.4 - Fix broken install command 0.8.3 - Fix broken install command 0.8.2 - Fix copy-paste error in word vector aggregation ([#118](https://github.com/textpipe/textpipe/issues/118)) 0.8.1 - Fixes bugs in several operations that didn't accept kwargs 0.8.0 - Bumps Spacy to 2.1 0.7.2 - Pins Spacy and Pattern versions (with pinned lxml) 0.7.0 - change operation's registry from list to dict - global pipeline data is available across operations via the `context` kwarg - load custom operations using `register_operation` in pipeline - custom steps (operations) with arguments %package -n python3-textpipe Summary: textpipe: clean and extract metadata from text Provides: python-textpipe BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-textpipe # textpipe: clean and extract metadata from text [![Build Status](https://travis-ci.com/textpipe/textpipe.svg?branch=master)](https://travis-ci.com/textpipe/textpipe) ![The textpipe logo](https://avatars3.githubusercontent.com/u/40492530?s=400&u=c65c2c8274cbdcd05b1942d1963d7aa2800e6d7f&v=4) `textpipe` is a Python package for converting raw text in to clean, readable text and extracting metadata from that text. Its functionalities include transforming raw text into readable text by removing HTML tags and extracting metadata such as the number of words and named entities from the text. ## Vision: the zen of textpipe - Designed for use in production pipelines without adult supervision. - Rechargeable batteries included: provide sane defaults and clear examples to adapt. - A uniform interface with thin wrappers around state-of-the-art NLP packages. - As language-agnostic as possible. - Bring your own models. ## Features - Clean raw text by removing `HTML` and other unreadable constructs - Identify the language of text - Extract the number of words, number of sentences, named entities from a text - Calculate the complexity of a text - Obtain text metadata by specifying a pipeline containing all desired elements - Obtain sentiment (polarity and a subjectivity score) - Generates word counts - Computes minhash for cheap similarity estimation of documents ## Installation It is recommended that you install textpipe using a virtual environment. - First, create your virtual environment using [virtualenv](https://virtualenv.pypa.io/en/stable/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/). - Using Venv if your default interpreter is python3.6 ```bash python3 -m venv .venv ``` - Using virtualenv. ```bash virtualenv venv -p python3.6 ``` - Using virtualenvwrapper ```bash mkvirtualenv textpipe -p python3.6 ``` - Install textpipe using pip. ```bash pip install textpipe ``` - Install the required packages using *requirements.txt*. ```bash pip install -r requirements.txt ``` ### A note on spaCy download model requirement While the requirements.txt file that comes with the package calls for spaCy's en_core_web_sm model, this can be changed depending on the model and language you require for your intended use. See [spaCy.io's page on their different models](https://spacy.io/usage/models) for more information. ## Usage example ```python >>> from textpipe import doc, pipeline >>> sample_text = 'Sample text! ' >>> document = doc.Doc(sample_text) >>> print(document.clean) 'Sample text!' >>> print(document.language) 'en' >>> print(document.nwords) 2 >>> pipe = pipeline.Pipeline(['CleanText', 'NWords']) >>> print(pipe(sample_text)) {'CleanText': 'Sample text!', 'NWords': 3} ``` In order to extend the existing Textpipe operations with your own proprietary operations; ```python test_pipe = pipeline.Pipeline(['CleanText', 'NWords']) def custom_op(doc, context=None, settings=None, **kwargs): return 1 custom_argument = {'argument' :1 } test_pipe.register_operation('CUSTOM_STEP', custom_op) test_pipe.steps.append(('CUSTOM_STEP', custom_argument )) ``` ## Contributing See [CONTRIBUTING](CONTRIBUTING.md) for guidelines for contributors. ## Changes 0.12.1 - Bumps redis, tqdm, pyling 0.12.0 - Bumps versions of many dependencies including textacy. Results for keyterm extraction changed. 0.11.9 - Exposes arbitrary SpaCy `ents` properties 0.11.8 - Exposes SpaCy's `cats` attribute 0.11.7 - Bumps spaCy and redis versions 0.11.6 - Fixes bug where gensim model is not cached in pipeline 0.11.5 - Raise TextpipeMissingModelException instead of KeyError 0.11.4 - Bumps spaCy and datasketch dependencies 0.11.1 - Replaces codacy with pylint on CI - Fixes pylint issues 0.11.0 - Adds wrapper around Gensim keyed vectors to construct document embeddings from Redis cache 0.9.0 - Adds functionality to compute document embeddings using a Gensim word2vec model 0.8.6 - Removes non standard utf chars before detecting language 0.8.5 - Bump spaCy to 2.1.3 0.8.4 - Fix broken install command 0.8.3 - Fix broken install command 0.8.2 - Fix copy-paste error in word vector aggregation ([#118](https://github.com/textpipe/textpipe/issues/118)) 0.8.1 - Fixes bugs in several operations that didn't accept kwargs 0.8.0 - Bumps Spacy to 2.1 0.7.2 - Pins Spacy and Pattern versions (with pinned lxml) 0.7.0 - change operation's registry from list to dict - global pipeline data is available across operations via the `context` kwarg - load custom operations using `register_operation` in pipeline - custom steps (operations) with arguments %package help Summary: Development documents and examples for textpipe Provides: python3-textpipe-doc %description help # textpipe: clean and extract metadata from text [![Build Status](https://travis-ci.com/textpipe/textpipe.svg?branch=master)](https://travis-ci.com/textpipe/textpipe) ![The textpipe logo](https://avatars3.githubusercontent.com/u/40492530?s=400&u=c65c2c8274cbdcd05b1942d1963d7aa2800e6d7f&v=4) `textpipe` is a Python package for converting raw text in to clean, readable text and extracting metadata from that text. Its functionalities include transforming raw text into readable text by removing HTML tags and extracting metadata such as the number of words and named entities from the text. ## Vision: the zen of textpipe - Designed for use in production pipelines without adult supervision. - Rechargeable batteries included: provide sane defaults and clear examples to adapt. - A uniform interface with thin wrappers around state-of-the-art NLP packages. - As language-agnostic as possible. - Bring your own models. ## Features - Clean raw text by removing `HTML` and other unreadable constructs - Identify the language of text - Extract the number of words, number of sentences, named entities from a text - Calculate the complexity of a text - Obtain text metadata by specifying a pipeline containing all desired elements - Obtain sentiment (polarity and a subjectivity score) - Generates word counts - Computes minhash for cheap similarity estimation of documents ## Installation It is recommended that you install textpipe using a virtual environment. - First, create your virtual environment using [virtualenv](https://virtualenv.pypa.io/en/stable/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/). - Using Venv if your default interpreter is python3.6 ```bash python3 -m venv .venv ``` - Using virtualenv. ```bash virtualenv venv -p python3.6 ``` - Using virtualenvwrapper ```bash mkvirtualenv textpipe -p python3.6 ``` - Install textpipe using pip. ```bash pip install textpipe ``` - Install the required packages using *requirements.txt*. ```bash pip install -r requirements.txt ``` ### A note on spaCy download model requirement While the requirements.txt file that comes with the package calls for spaCy's en_core_web_sm model, this can be changed depending on the model and language you require for your intended use. See [spaCy.io's page on their different models](https://spacy.io/usage/models) for more information. ## Usage example ```python >>> from textpipe import doc, pipeline >>> sample_text = 'Sample text! ' >>> document = doc.Doc(sample_text) >>> print(document.clean) 'Sample text!' >>> print(document.language) 'en' >>> print(document.nwords) 2 >>> pipe = pipeline.Pipeline(['CleanText', 'NWords']) >>> print(pipe(sample_text)) {'CleanText': 'Sample text!', 'NWords': 3} ``` In order to extend the existing Textpipe operations with your own proprietary operations; ```python test_pipe = pipeline.Pipeline(['CleanText', 'NWords']) def custom_op(doc, context=None, settings=None, **kwargs): return 1 custom_argument = {'argument' :1 } test_pipe.register_operation('CUSTOM_STEP', custom_op) test_pipe.steps.append(('CUSTOM_STEP', custom_argument )) ``` ## Contributing See [CONTRIBUTING](CONTRIBUTING.md) for guidelines for contributors. ## Changes 0.12.1 - Bumps redis, tqdm, pyling 0.12.0 - Bumps versions of many dependencies including textacy. Results for keyterm extraction changed. 0.11.9 - Exposes arbitrary SpaCy `ents` properties 0.11.8 - Exposes SpaCy's `cats` attribute 0.11.7 - Bumps spaCy and redis versions 0.11.6 - Fixes bug where gensim model is not cached in pipeline 0.11.5 - Raise TextpipeMissingModelException instead of KeyError 0.11.4 - Bumps spaCy and datasketch dependencies 0.11.1 - Replaces codacy with pylint on CI - Fixes pylint issues 0.11.0 - Adds wrapper around Gensim keyed vectors to construct document embeddings from Redis cache 0.9.0 - Adds functionality to compute document embeddings using a Gensim word2vec model 0.8.6 - Removes non standard utf chars before detecting language 0.8.5 - Bump spaCy to 2.1.3 0.8.4 - Fix broken install command 0.8.3 - Fix broken install command 0.8.2 - Fix copy-paste error in word vector aggregation ([#118](https://github.com/textpipe/textpipe/issues/118)) 0.8.1 - Fixes bugs in several operations that didn't accept kwargs 0.8.0 - Bumps Spacy to 2.1 0.7.2 - Pins Spacy and Pattern versions (with pinned lxml) 0.7.0 - change operation's registry from list to dict - global pipeline data is available across operations via the `context` kwarg - load custom operations using `register_operation` in pipeline - custom steps (operations) with arguments %prep %autosetup -n textpipe-0.12.2 %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-textpipe -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 0.12.2-1 - Package Spec generated