gensim

gensim – Topic Modelling in Python
==================================

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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.

## ⚠️ Want to help out? [Sponsor Gensim](https://github.com/sponsors/piskvorky) ❤️

## ⚠️ Gensim is in stable maintenance mode: we are not accepting new features, but bug and documentation fixes are still welcome! ⚠️

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 (trivial
        streaming API)
    -   easy to extend with other Vector Space algorithms (trivial
        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].

If this feature list left you scratching your head, you can first read
more about the [Vector Space Model] and [unsupervised document analysis]
on Wikipedia.

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

This software depends on [NumPy], a Python package for
scientific computing. Please bear in mind that building NumPy from source
(e.g. by installing gensim on a platform which lacks NumPy .whl distribution)
is a non-trivial task involving [linking NumPy to a BLAS library].  
It is recommended to provide a fast one (such as MKL, [ATLAS] or
[OpenBLAS]) which can improve performance by as much as an order of
magnitude. On OSX, NumPy picks up its vecLib BLAS automatically,
so you don’t