gensim README

The project's README from github.com/piskvorky/gensim. As one markdown file: README.md.

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 ❤️

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

Features

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 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 package:

    tar -xvzf gensim-X.X.X.tar.gz
    cd gensim-X.X.X/
    pip install .

For alternative modes of installation, see the documentation.

Gensim is being continuously tested under all supported Python versions. 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, and is a central feature of gensim, rather than something bolted on as an afterthought.

Documentation

Support

For commercial support, please see Gensim sponsorship.

Ask open-ended questions on the public Gensim Mailing List.

Raise bugs on Github but please make sure you follow the issue template. Issues that are not bugs or fail to provide the requested details will be closed without inspection.

Adopters

Company Logo Industry Use of Gensim
RARE Technologies rare ML & NLP consulting Creators of Gensim – this is us!
Amazon amazon Retail Document similarity.
National Institutes of Health nih Health Processing grants and publications with word2vec.
Cisco Security cisco Security Large-scale fraud detection.
Mindseye mindseye Legal Similarities in legal documents.
Channel 4 channel4 Media Recommendation engine.
Talentpair talent-pair HR Candidate matching in high-touch recruiting.
Juju juju HR Provide non-obvious related job suggestions.
Tailwind tailwind Media Post interesting and relevant content to Pinterest.
Issuu issuu 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 search-metrics Content Marketing Gensim word2vec used for entity disambiguation in Search Engine Optimisation.
12K Research 12k Media Document similarity analysis on media articles.
Stillwater Supercomputing stillwater Hardware Document comprehension and association with word2vec.
SiteGround siteground Web hosting An ensemble search engine which uses different embeddings models and similarities, including word2vec, WMD, and LDA.
Capital One capitalone Finance Topic modeling for customer complaints exploration.

Citing gensim

When citing gensim in academic papers and theses, 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},
      note={\url{http://is.muni.cz/publication/884893/en}},
      language={English}
}

gensim (piskvorky/gensim)

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16.5K ·GitHub
Gensim
What is this?Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora, aimed at the natural language processing (NLP) and information retrieval (IR) community.
So is it a library I import into my Python code, or a service I run?It's a library you import into your Python code - no server or UI. You plug in your input corpus through a streaming API and can extend it with other vector-space algorithms via a transformation API.
What do I need besides Python itself?It depends on NumPy. If a NumPy wheel isn't available and it has to build from source, linking it to a fast BLAS such as MKL, ATLAS or OpenBLAS can improve performance by an order of magnitude; on OSX, NumPy picks up vecLib BLAS automatically.
Is the project still being actively developed?Gensim is in stable maintenance mode: new features are no longer accepted, though bug and documentation fixes are still welcome. Python 2.7 support was dropped in 4.0.0 - install gensim 3.8.3 if you must stay on Python 2.7.
Who uses gensim in production?The README's adopters list includes Amazon (document similarity), the National Institutes of Health (word2vec on grants and publications), Cisco Security (large-scale fraud detection), Capital One (topic modelling of customer complaints) and Issuu, whose LDA module sits at the core of analysing each uploaded publication.
Can it use more than one machine?Yes. Beyond multicore implementations, gensim supports distributed computing - Latent Semantic Analysis and Latent Dirichlet Allocation can run on a cluster of computers.
Where should a beginner start?The official documentation offers a QuickStart, tutorials and Jupyter Notebook tutorials; if the feature list is overwhelming, the README points to Wikipedia explainers on the Vector Space Model and unsupervised document analysis.
What are similar projects?Chinese-Word-Vectors, fastText, sutskever-30-implementations, DeepLearning, flair, numpy-ml, LLMSearchRecommender, funNLP
Which topics is it in?word-embeddings, word2vec, information-retrieval, data-mining, numpy, natural-language-processing, semantic-search, embeddings, neural-network, academic-research, nlp, data-science
Creator
piskvorky
Size
104.1 MB
Stars
16.5K
Updated
CommitOct 16Release4.4.0Oct 16
Age
15y
Commits · 90 Days