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Sync PyPi package description with Github README #3552

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update pypi package long description to always track README
jicruz96 Jul 18, 2024
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Merge branch 'piskvorky:develop' into develop
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update README image paths
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dynamically update image paths in README during setup.py build to use…
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31 changes: 15 additions & 16 deletions README.md
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Expand Up @@ -123,21 +123,21 @@ Adopters

| Company | Logo | Industry | Use of Gensim |
|---------|------|----------|---------------|
| [RARE Technologies](https://rare-technologies.com/) | ![rare](docs/src/readme_images/rare.png) | ML & NLP consulting | Creators of Gensim – this is us! |
| [Amazon](http://www.amazon.com/) | ![amazon](docs/src/readme_images/amazon.png) | Retail | Document similarity. |
| [National Institutes of Health](https://github.com/NIHOPA/pipeline_word2vec) | ![nih](docs/src/readme_images/nih.png) | Health | Processing grants and publications with word2vec. |
| [Cisco Security](http://www.cisco.com/c/en/us/products/security/index.html) | ![cisco](docs/src/readme_images/cisco.png) | Security | Large-scale fraud detection. |
| [Mindseye](http://www.mindseyesolutions.com/) | ![mindseye](docs/src/readme_images/mindseye.png) | Legal | Similarities in legal documents. |
| [Channel 4](http://www.channel4.com/) | ![channel4](docs/src/readme_images/channel4.png) | Media | Recommendation engine. |
| [Talentpair](http://talentpair.com) | ![talent-pair](docs/src/readme_images/talent-pair.png) | HR | Candidate matching in high-touch recruiting. |
| [Juju](http://www.juju.com/) | ![juju](docs/src/readme_images/juju.png) | HR | Provide non-obvious related job suggestions. |
| [Tailwind](https://www.tailwindapp.com/) | ![tailwind](docs/src/readme_images/tailwind.png) | Media | Post interesting and relevant content to Pinterest. |
| [Issuu](https://issuu.com/) | ![issuu](docs/src/readme_images/issuu.png) | Media | Gensim's LDA module lies at the very core of the analysis we perform on each uploaded publication to figure out what it's all about. |
| [Search Metrics](http://www.searchmetrics.com/) | ![search-metrics](docs/src/readme_images/search-metrics.png) | Content Marketing | Gensim word2vec used for entity disambiguation in Search Engine Optimisation. |
| [12K Research](https://12k.com/) | ![12k](docs/src/readme_images/12k.png)| Media | Document similarity analysis on media articles. |
| [Stillwater Supercomputing](http://www.stillwater-sc.com/) | ![stillwater](docs/src/readme_images/stillwater.png) | Hardware | Document comprehension and association with word2vec. |
| [SiteGround](https://www.siteground.com/) | ![siteground](docs/src/readme_images/siteground.png) | Web hosting | An ensemble search engine which uses different embeddings models and similarities, including word2vec, WMD, and LDA. |
| [Capital One](https://www.capitalone.com/) | ![capitalone](docs/src/readme_images/capitalone.png) | Finance | Topic modeling for customer complaints exploration. |
| [RARE Technologies](https://rare-technologies.com/) | ![rare](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/rare.png) | ML & NLP consulting | Creators of Gensim – this is us! |
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| [Amazon](http://www.amazon.com/) | ![amazon](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/amazon.png) | Retail | Document similarity. |
| [National Institutes of Health](https://github.com/NIHOPA/pipeline_word2vec) | ![nih](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/nih.png) | Health | Processing grants and publications with word2vec. |
| [Cisco Security](http://www.cisco.com/c/en/us/products/security/index.html) | ![cisco](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/cisco.png) | Security | Large-scale fraud detection. |
| [Mindseye](http://www.mindseyesolutions.com/) | ![mindseye](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/mindseye.png) | Legal | Similarities in legal documents. |
| [Channel 4](http://www.channel4.com/) | ![channel4](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/channel4.png) | Media | Recommendation engine. |
| [Talentpair](http://talentpair.com) | ![talent-pair](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/talent-pair.png) | HR | Candidate matching in high-touch recruiting. |
| [Juju](http://www.juju.com/) | ![juju](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/juju.png) | HR | Provide non-obvious related job suggestions. |
| [Tailwind](https://www.tailwindapp.com/) | ![tailwind](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/tailwind.png) | Media | Post interesting and relevant content to Pinterest. |
| [Issuu](https://issuu.com/) | ![issuu](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/issuu.png) | Media | Gensim's LDA module lies at the very core of the analysis we perform on each uploaded publication to figure out what it's all about. |
| [Search Metrics](http://www.searchmetrics.com/) | ![search-metrics](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/search-metrics.png) | Content Marketing | Gensim word2vec used for entity disambiguation in Search Engine Optimisation. |
| [12K Research](https://12k.com/) | ![12k](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/12k.png)| Media | Document similarity analysis on media articles. |
| [Stillwater Supercomputing](http://www.stillwater-sc.com/) | ![stillwater](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/stillwater.png) | Hardware | Document comprehension and association with word2vec. |
| [SiteGround](https://www.siteground.com/) | ![siteground](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/siteground.png) | Web hosting | An ensemble search engine which uses different embeddings models and similarities, including word2vec, WMD, and LDA. |
| [Capital One](https://www.capitalone.com/) | ![capitalone](https://raw.githubusercontent.com/piskvorky/gensim/develop/docs/src/readme_images/capitalone.png) | Finance | Topic modeling for customer complaints exploration. |

-------

Expand Down Expand Up @@ -179,4 +179,3 @@ BibTeX entry:
[OpenBLAS]: https://xianyi.github.io/OpenBLAS/
[source tar.gz]: https://pypi.org/project/gensim/
[documentation]: https://radimrehurek.com/gensim/#install

111 changes: 3 additions & 108 deletions setup.py
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Expand Up @@ -16,6 +16,7 @@
import shutil
import sys
from collections import OrderedDict
from pathlib import Path

from setuptools import Extension, find_packages, setup, distutils
from setuptools.command.build_ext import build_ext
Expand Down Expand Up @@ -158,112 +159,6 @@ def run(self):
cmdclass.update(vars(wheelhouse_uploader.cmd))


LONG_DESCRIPTION = u"""
==============================================
gensim -- Topic Modelling in Python
==============================================

|GA|_
|Wheel|_

.. |GA| image:: https://github.com/RaRe-Technologies/gensim/actions/workflows/tests.yml/badge.svg?branch=develop
.. |Wheel| image:: https://img.shields.io/pypi/wheel/gensim.svg

.. _GA: https://github.com/RaRe-Technologies/gensim/actions
.. _Downloads: https://pypi.org/project/gensim/
.. _License: https://radimrehurek.com/gensim/intro.html#licensing
.. _Wheel: https://pypi.org/project/gensim/

Gensim is a Python library for *topic modelling*, *document indexing* and *similarity retrieval* with large corpora.
Target audience is the *natural language processing* (NLP) and *information retrieval* (IR) community.

Features
---------

* All algorithms are **memory-independent** w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core)
* **Intuitive interfaces**

* easy to plug in your own input corpus/datastream (simple streaming API)
* easy to extend with other Vector Space algorithms (simple transformation API)

* Efficient multicore implementations of popular algorithms, such as online **Latent Semantic Analysis (LSA/LSI/SVD)**,
**Latent Dirichlet Allocation (LDA)**, **Random Projections (RP)**, **Hierarchical Dirichlet Process (HDP)** or **word2vec deep learning**.
* **Distributed computing**: can run *Latent Semantic Analysis* and *Latent Dirichlet Allocation* on a cluster of computers.
* Extensive `documentation and Jupyter Notebook tutorials <https://github.com/RaRe-Technologies/gensim/#documentation>`_.


If this feature list left you scratching your head, you can first read more about the `Vector
Space Model <https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
document analysis <https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.

Installation
------------

This software depends on `NumPy and Scipy <https://scipy.org/install/>`_, two Python packages for scientific computing.
You must have them installed prior to installing `gensim`.

It is also recommended you install a fast BLAS library before installing NumPy. This is optional, but using an optimized BLAS such as MKL, `ATLAS <https://math-atlas.sourceforge.net/>`_ or `OpenBLAS <https://xianyi.github.io/OpenBLAS/>`_ is known to improve performance by as much as an order of magnitude. On OSX, NumPy picks up its vecLib BLAS automatically, so you don't need to do anything special.

Install the latest version of gensim::

pip install --upgrade gensim

Or, if you have instead downloaded and unzipped the `source tar.gz <https://pypi.org/project/gensim/>`_ package::

python setup.py install


For alternative modes of installation, see the `documentation <https://radimrehurek.com/gensim/#install>`_.

Gensim is being `continuously tested <https://radimrehurek.com/gensim/#testing>`_ under all `supported Python versions <https://github.com/RaRe-Technologies/gensim/wiki/Gensim-And-Compatibility>`_.
Support for Python 2.7 was dropped in gensim 4.0.0 – install gensim 3.8.3 if you must use Python 2.7.


How come gensim is so fast and memory efficient? Isn't it pure Python, and isn't Python slow and greedy?
--------------------------------------------------------------------------------------------------------

Many scientific algorithms can be expressed in terms of large matrix operations (see the BLAS note above). Gensim taps into these low-level BLAS libraries, by means of its dependency on NumPy. So while gensim-the-top-level-code is pure Python, it actually executes highly optimized Fortran/C under the hood, including multithreading (if your BLAS is so configured).

Memory-wise, gensim makes heavy use of Python's built-in generators and iterators for streamed data processing. Memory efficiency was one of gensim's `design goals <https://radimrehurek.com/gensim/intro.html#design-principles>`_, and is a central feature of gensim, rather than something bolted on as an afterthought.

Documentation
-------------
* `QuickStart`_
* `Tutorials`_
* `Tutorial Videos`_
* `Official Documentation and Walkthrough`_

Citing gensim
-------------

When `citing gensim in academic papers and theses <https://scholar.google.cz/citations?view_op=view_citation&hl=en&user=9vG_kV0AAAAJ&citation_for_view=9vG_kV0AAAAJ:u-x6o8ySG0sC>`_, please use this BibTeX entry::

@inproceedings{rehurek_lrec,
title = {{Software Framework for Topic Modelling with Large Corpora}},
author = {Radim {\\v R}eh{\\r u}{\\v r}ek and Petr Sojka},
booktitle = {{Proceedings of the LREC 2010 Workshop on New
Challenges for NLP Frameworks}},
pages = {45--50},
year = 2010,
month = May,
day = 22,
publisher = {ELRA},
address = {Valletta, Malta},
language={English}
}

----------------

Gensim is open source software released under the `GNU LGPLv2.1 license <https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html>`_.
Copyright (c) 2009-now Radim Rehurek

.. _Official Documentation and Walkthrough: https://radimrehurek.com/gensim/
.. _Tutorials: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials
.. _Tutorial Videos: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#videos
.. _QuickStart: https://radimrehurek.com/gensim/gensim_numfocus/auto_examples/core/run_core_concepts.html

"""

distributed_env = ['Pyro4 >= 4.27']

visdom_req = ['visdom >= 0.1.8, != 0.1.8.7']
Expand Down Expand Up @@ -342,8 +237,8 @@ def run(self):
name='gensim',
version='4.3.2.dev0',
description='Python framework for fast Vector Space Modelling',
long_description=LONG_DESCRIPTION,

long_description=Path("README.md").read_text(),
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long_description_content_type='text/markdown',
ext_modules=ext_modules,
cmdclass=cmdclass,
packages=find_packages(),
Expand Down
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