clean up docs structure

docs/src/conf.py

@@ -54,7 +54,7 @@
 
 # General information about the project.
 project = u'gensim'
-copyright = u'2009-now Radim Řehůřek, https://radimrehurek.com.'
+copyright = u'2009-now'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the

docs/src/distributed.rst

@@ -1,3 +1,5 @@
+:orphan:
+
 .. _distributed:
 
 Distributed Computing

docs/src/indextoc.rst

@@ -3,8 +3,6 @@
    :maxdepth: 1
 
    intro
-   distributed
    auto_examples/index
-   support
-   wiki
    apiref
+   support

docs/src/intro.rst

@@ -1,17 +1,22 @@
 .. _intro:
 
-============
-Introduction
-============
+===============
+What is Gensim?
+===============
 
-Gensim is a :ref:`free <availability>` Python library designed to automatically extract semantic
-topics from documents, as efficiently (computer-wise) and painlessly (human-wise) as possible.
+Gensim is a free open-source Python library for representing
+documents as semantic vectors, as efficiently (computer-wise) and painlessly (human-wise) as possible.
 
-Gensim is designed to process raw, unstructured digital texts ("*plain text*").
+.. image:: _static/images/gensim_logo_positive_complete_tb.png
+  :width: 600
+  :alt: Gensim logo
+
+Gensim is designed to process raw, unstructured digital texts ("*plain text*") using unsupervised machine learning algorithms.
 
 The algorithms in Gensim, such as :class:`~gensim.models.word2vec.Word2Vec`, :class:`~gensim.models.fasttext.FastText`,
-Latent Semantic Analysis (LSI, LSA, see :class:`~gensim.models.lsimodel.LsiModel`), Latent Dirichlet
-Allocation (LDA, see :class:`~gensim.models.ldamodel.LdaModel`) etc, automatically discover the semantic structure of documents by examining statistical
+Latent Semantic Indexing (LSI, LSA, :class:`~gensim.models.lsimodel.LsiModel`), Latent Dirichlet
+Allocation (LDA, :class:`~gensim.models.ldamodel.LdaModel`) etc, automatically discover the semantic
+structure of documents by examining statistical
 co-occurrence patterns within a corpus of training documents. These algorithms are **unsupervised**,
 which means no human input is necessary -- you only need a corpus of plain text documents.
 
@@ -24,42 +29,88 @@ Once these statistical patterns are found, any plain text documents (sentence, p
 
 .. _design:
 
-Features
---------
+Design principles
+-----------------
+
+We built Gensim from scratch for:
 
+* **Practicality** -- as industry experts, we focus on proven, battle-hardened algorithms to solve real industry problems. More focus on engineering, less on academia.
 * **Memory independence** -- there is no need for the whole training corpus to
-  reside fully in RAM at any one time (can process large, web-scale corpora).
-* **Memory sharing** -- trained models can be persisted to disk and loaded back via `mmap <https://en.wikipedia.org/wiki/Mmap>`_. Multiple processes can share the same data, cutting down RAM footprint.
-* Efficient implementations for several popular vector space algorithms,
-  including :class:`~gensim.models.word2vec.Word2Vec`, :class:`~gensim.models.doc2vec.Doc2Vec`, :class:`~gensim.models.fasttext.FastText`,
-  TF-IDF, Latent Semantic Analysis (LSI, LSA, see :class:`~gensim.models.lsimodel.LsiModel`),
-  Latent Dirichlet Allocation (LDA, see :class:`~gensim.models.ldamodel.LdaModel`) or Random Projection (see :class:`~gensim.models.rpmodel.RpModel`).
-* I/O wrappers and readers from several popular data formats.
-* Fast similarity queries for documents in their semantic representation.
+  reside fully in RAM at any one time. Can process large, web-scale corpora using data streaming.
+* **Performance** – highly optimized implementations of popular vector space algorithms using C, BLAS and memory-mapping.
 
-The **principal design objectives** behind Gensim are:
 
-1. Straightforward interfaces and low API learning curve for developers. Good for prototyping.
-2. Memory independence with respect to the size of the input corpus; all intermediate
-   steps and algorithms operate in a streaming fashion, accessing one document
-   at a time.
+Installation
+------------
 
-.. seealso::
+Gensim is a Python library, so you need `Python <https://www.python.org/downloads/>`_. Gensim supports all Python versions that haven't reached their `end-of-life <https://devguide.python.org/#status-of-python-branches>`_.
 
-    We also built a high performance commercial server for NLP, document analysis, indexing, search and clustering: https://scaletext.ai. ScaleText is available both on-prem and as SaaS.
+If you need with an older Python (such as Python 2.7), you must install an older version of Gensim (such as `Gensim 3.8.3 <https://github.com/RaRe-Technologies/gensim/releases/tag/3.8.3>`_).
 
-    Reach out at [email protected] if you need an industry-grade NLP tool with professional support.
+To install gensim, simply run::
 
-.. _availability:
+  pip install --upgrade gensim
 
-Availability
-------------
+Alternatively, you can download the source code from `Github <https://github.com/RARE-Technologies/gensim/>`__
+or the `Python Package Index <http://pypi.python.org/pypi/gensim>`_.
+
+After installation, learn how to use Gensim from its :ref:`sphx_glr_auto_examples_core_run_core_concepts.py` tutorials.
+
+
+.. _Licensing:
+
+Licensing
+----------
+
+Gensim is licensed under the OSI-approved `GNU LGPLv2.1 license <http://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html>`_.
+This means that it's free for both personal and commercial use, but if you make any
+modification to Gensim that you distribute to other people, you have to disclose
+the source code of these modifications.
+
+Apart from that, you are free to redistribute Gensim in any way you like, though you're
+not allowed to modify its license (doh!).
+
+If LGPL doesn't fit your bill, you can ask for :ref:`Commercial support`.
+
+.. _Academic citing:
+
+Academic citing
+---------------
+
+Gensim has been used in `over two thousand research papers and student theses <https://scholar.google.com/citations?view_op=view_citation&hl=en&user=9vG_kV0AAAAJ&citation_for_view=9vG_kV0AAAAJ:NaGl4SEjCO4C>`_.
+
+When citing Gensim, please use `this BibTeX entry <bibtex_gensim.bib>`_::
+
+  @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},
+        note={\url{http://is.muni.cz/publication/884893/en}},
+        language={English}
+  }
+
+Gensim = "Generate Similar"
+---------------------------
+
+Historically, Gensim started off as a collection of Python scripts for the Czech Digital Mathematics Library `dml.cz <http://dml.cz/>`_ project, back in 2008. The scripts served to generate a short list of the most similar math articles to a given article.
+
+I (Radim) also wanted to try these fancy "Latent Semantic Methods", but the libraries that realized the necessary computation were `not much fun to work with <http://soi.stanford.edu/~rmunk/PROPACK/>`_.
 
-Gensim is licensed under the OSI-approved `GNU LGPLv2.1 license <http://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html>`_ and can be downloaded either from its `Github repository <https://github.com/RARE-Technologies/gensim/>`_
-or from the `Python Package Index <http://pypi.python.org/pypi/gensim>`_.
+Naturally, I set out to reinvent the wheel. Our `2010 LREC publication <http://radimrehurek.com/lrec2010_final.pdf>`_ describes the initial design decisions behind Gensim: **clarity, efficiency and scalability**. It is fairly representative of how Gensim works even today.
 
+Later versions of Gensim improved this efficiency and scalability tremendously. In fact, I made algorithmic scalability of distributional semantics the topic of my `PhD thesis <http://radimrehurek.com/phd_rehurek.pdf>`_.
 
-Core concepts
--------------
+By now, Gensim is---to my knowledge---the most robust, efficient and hassle-free piece
+of software to realize unsupervised semantic modelling from plain text. It stands
+in contrast to brittle homework-assignment-implementations that do not scale on one hand,
+and robust java-esque projects that take forever just to run "hello world".
 
-See the :ref:`sphx_glr_auto_examples_core_run_core_concepts.py` tutorial.
+In 2011, I moved Gensim's source code to `Github <https://github.com/piskvorky/gensim>`__
+and created the Gensim website. In 2013 Gensim got its current logo, and in 2020 a website redesign.

docs/src/sphinx_rtd_theme/advertisement.html

@@ -2,4 +2,5 @@
   <h3>Get Expert Help From The Gensim Authors</h3>
   <p>• <a href="https://rare-technologies.com/">Consulting</a> in Machine Learning &amp; NLP</p>
   <p>• <a href="https://rare-technologies.com/corporate-training/">Corporate trainings</a> in Data Science, NLP and Deep Learning</p>
+  <p>• <a href="https://pii-tools.com">PII Tools</a> automated discovery of personal and sensitive data</p>
 </div>

docs/src/sphinx_rtd_theme/layout.html

@@ -234,10 +234,10 @@
               {%- if hasdoc('copyright') %}
                 {% set path = pathto('copyright') %}
                 {% set copyright = copyright|e %}
-                &copy; <a href="{{ path }}">{% trans %}Copyright{% endtrans %}</a> {{ copyright }}
+                &copy; <a href="{{ path }}">{% trans %}Copyright{% endtrans %}</a> {{ copyright }}, <a href="https://radimrehurek.com">Radim Řehůřek</a>.
               {%- else %}
                 {% set copyright = copyright|e %}
-                &copy; {% trans %}Copyright{% endtrans %} {{ copyright }}
+                &copy; {% trans %}Copyright{% endtrans %} {{ copyright }}, <a href="https://radimrehurek.com">Radim Řehůřek</a>.
               {%- endif %}
             {%- endif %}
 
@@ -257,11 +257,6 @@
                         {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}
                       </span>
             {%- endif %}
-            <br>
-            <a target="_blank" rel="nofollow" href="https://radimrehurek.com/"> Radim Řehůřek – Machine learing and data
-              mining expert</a>
-            <br>
-            <a target="_blank" rel="nofollow" href="https://edgy.digital/"> Created by edgy.digital</a>
           </div>
           <nav id="social-menu" class="menu-footer-container">
           </nav>

docs/src/sphinx_rtd_theme/layouthome.html

@@ -100,7 +100,7 @@ <h5 class="themecolor">Do you have a question?</h5>
               <h3>Gensim Support</h3>
               <hr class="no_line" style="margin:0 auto 30px">
               <!--<h4 style="display: inline-block; font-weight: 300; margin-right: 10px; color: #000;"><i class="icon-mobile themecolor" style></i> +61 (0) 383 766 284</h4>-->
-              <h4 style="display: inline-block; font-weight: 300; color: #000;">See the <a href="{{ pathto('support') }}">Gensim support page</a> for how to get open source and commercial support.</h4>
+              <h4 style="display: inline-block; font-weight: 300; color: #000;">See the <a href="{{ pathto('support') }}">Gensim support page</a> for how to ask for open source and commercial support.</h4>
             </div>
           </aside>
         </div>
@@ -123,10 +123,10 @@ <h4 style="display: inline-block; font-weight: 300; color: #000;">See the <a hre
               {%- if hasdoc('copyright') %}
                 {% set path = pathto('copyright') %}
                 {% set copyright = copyright|e %}
-                &copy; <a href="{{ path }}">{% trans %}Copyright{% endtrans %}</a> {{ copyright }}
+                &copy; <a href="{{ path }}">{% trans %}Copyright{% endtrans %}</a> {{ copyright }}, <a href="https://radimrehurek.com">Radim Řehůřek</a>.
               {%- else %}
                 {% set copyright = copyright|e %}
-                &copy; {% trans %}Copyright{% endtrans %} {{ copyright }}
+                &copy; {% trans %}Copyright{% endtrans %} {{ copyright }}, <a href="https://radimrehurek.com">Radim Řehůřek</a>.
               {%- endif %}
             {%- endif %}
 

docs/src/sphinx_rtd_theme/topbar.html

@@ -29,16 +29,16 @@
                 <a href="{{ pathto("index") }}"><span>Home</span></a>
               </li>
               <li{% if pagename == "auto_examples/index" %} class="current-menu-item"{% endif %}>
-                <a href="{{ pathto("auto_examples/index") }}"><span>Documentation</span></a>
+                <a href="{{ pathto("auto_examples/index") }}#documentation"><span>Documentation</span></a>
               </li>
               <li{% if pagename == "support" %} class="current-menu-item"{% endif %}>
-                <a href="{{ pathto("support") }}"><span>Support</span></a>
+                <a href="{{ pathto("support") }}#support"><span>Support</span></a>
               </li>
               <li{% if pagename == "apiref" %} class="current-menu-item"{% endif %}>
-                <a href="{{ pathto("apiref") }}"><span>API</span></a>
+                <a href="{{ pathto("apiref") }}#api-reference"><span>API</span></a>
               </li>
-              <li{% if pagename == "about" %} class="current-menu-item"{% endif %}>
-                <a href="{{ pathto("about") }}"><span>About</span></a>
+              <li{% if pagename == "intro" %} class="current-menu-item"{% endif %}>
+                <a href="{{ pathto("intro") }}#what-is-gensim"><span>About</span></a>
               </li>
             </ul>
           </nav>