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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Gensim
Что это такое?Gensim — это Python-библиотека для тематического моделирования, индексации документов и поиска похожих текстов в больших корпусах. Она создана для сообществ, занимающихся обработкой естественного языка (NLP) и информационным поиском (IR).
То есть это библиотека, которую я импортирую в свой Python-код, а не сервис, который нужно запускать?Да, это библиотека, которую вы импортируете в свой Python-код — без сервера и интерфейса. Входной корпус подаётся через стриминговый API, а расширять её другими алгоритмами векторного пространства можно через transformation API.
Что мне нужно, кроме самого Python?Нужна библиотека NumPy. Если готовый NumPy wheel недоступен и его приходится собирать из исходников, линковка с быстрой BLAS-реализацией вроде MKL, ATLAS или OpenBLAS может ускорить работу на порядок; на macOS NumPy автоматически подхватывает vecLib BLAS.
Проект ещё активно развивается?Gensim находится в режиме стабильной поддержки: новые фичи больше не принимаются, но исправления багов и документации по-прежнему приветствуются. Поддержка Python 2.7 прекращена с версии 4.0.0 — если нужно остаться на Python 2.7, ставьте gensim 3.8.3.
Кто использует gensim в продакшене?В списке внедрений из README значатся Amazon (поиск похожих документов), Национальные институты здравоохранения США (word2vec для грантов и публикаций), Cisco Security (выявление мошенничества в больших масштабах), Capital One (тематическое моделирование жалоб клиентов) и Issuu, чей LDA-модуль лежит в основе анализа каждой загруженной публикации.
А можно задействовать больше одной машины?Да. Помимо реализаций под многоядерные процессоры, gensim поддерживает распределённые вычисления — латентно-семантический анализ (LSA) и латентное размещение Дирихле (LDA) можно запускать на кластере компьютеров.
С чего новичку лучше начать?В официальной документации есть QuickStart, обучающие материалы и туториалы в виде Jupyter Notebook. Если список возможностей сбивает с толку, README отсылает к статьям в Википедии о векторной модели (Vector Space Model) и обучении без учителя для анализа документов.
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