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https://github.com/piskvorky/gensim
Topic Modelling for Humans
https://github.com/piskvorky/gensim
data-mining data-science document-similarity fasttext gensim information-retrieval machine-learning natural-language-processing neural-network nlp python topic-modeling word-embeddings word-similarity word2vec
Last synced: 4 days ago
JSON representation
Topic Modelling for Humans
- Host: GitHub
- URL: https://github.com/piskvorky/gensim
- Owner: piskvorky
- License: lgpl-2.1
- Created: 2011-02-10T07:43:04.000Z (almost 14 years ago)
- Default Branch: develop
- Last Pushed: 2024-04-24T11:04:13.000Z (8 months ago)
- Last Synced: 2024-05-01T19:40:59.250Z (8 months ago)
- Topics: data-mining, data-science, document-similarity, fasttext, gensim, information-retrieval, machine-learning, natural-language-processing, neural-network, nlp, python, topic-modeling, word-embeddings, word-similarity, word2vec
- Language: Python
- Homepage: https://radimrehurek.com/gensim
- Size: 101 MB
- Stars: 15,255
- Watchers: 436
- Forks: 4,345
- Open Issues: 409
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: COPYING
- Citation: CITATION.cff
- Security: SECURITY.md
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README
gensim – Topic Modelling in Python
==================================
[](https://github.com/RaRe-Technologies/gensim/actions)
[](https://github.com/RaRe-Technologies/gensim/releases)
[](https://pepy.tech/project/gensim/)
[](https://doi.org/10.13140/2.1.2393.1847)
[](https://groups.google.com/g/gensim)
[](https://twitter.com/gensim_py)
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 need to do anything special.
Install the latest version of gensim:
```bash
pip install --upgrade gensim
```
Or, if you have instead downloaded and unzipped the [source tar.gz]
package:
```bash
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](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], and is a central feature of gensim, rather than
something bolted on as an afterthought.
Documentation
-------------
- [QuickStart]
- [Tutorials]
- [Official API Documentation]
[QuickStart]: https://radimrehurek.com/gensim/auto_examples/core/run_core_concepts.html
[Tutorials]: https://radimrehurek.com/gensim/auto_examples/
[Official Documentation and Walkthrough]: https://radimrehurek.com/gensim/
[Official API Documentation]: https://radimrehurek.com/gensim/auto_examples/index.html#documentation
Support
-------
For commercial support, please see [Gensim sponsorship](https://github.com/sponsors/piskvorky).
Ask open-ended questions on the public [Gensim Mailing List](https://groups.google.com/g/gensim).
Raise bugs on [Github](https://github.com/RaRe-Technologies/gensim/blob/develop/CONTRIBUTING.md) but please **make sure you follow the [issue template](https://github.com/RaRe-Technologies/gensim/blob/develop/ISSUE_TEMPLATE.md)**. 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](https://rare-technologies.com/) |  | ML & NLP consulting | Creators of Gensim – this is us! |
| [Amazon](http://www.amazon.com/) |  | Retail | Document similarity. |
| [National Institutes of Health](https://github.com/NIHOPA/pipeline_word2vec) |  | Health | Processing grants and publications with word2vec. |
| [Cisco Security](http://www.cisco.com/c/en/us/products/security/index.html) |  | Security | Large-scale fraud detection. |
| [Mindseye](http://www.mindseyesolutions.com/) |  | Legal | Similarities in legal documents. |
| [Channel 4](http://www.channel4.com/) |  | Media | Recommendation engine. |
| [Talentpair](http://talentpair.com) |  | HR | Candidate matching in high-touch recruiting. |
| [Juju](http://www.juju.com/) |  | HR | Provide non-obvious related job suggestions. |
| [Tailwind](https://www.tailwindapp.com/) |  | Media | Post interesting and relevant content to Pinterest. |
| [Issuu](https://issuu.com/) |  | 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/) |  | Content Marketing | Gensim word2vec used for entity disambiguation in Search Engine Optimisation. |
| [12K Research](https://12k.com/) | | Media | Document similarity analysis on media articles. |
| [Stillwater Supercomputing](http://www.stillwater-sc.com/) |  | Hardware | Document comprehension and association with word2vec. |
| [SiteGround](https://www.siteground.com/) |  | Web hosting | An ensemble search engine which uses different embeddings models and similarities, including word2vec, WMD, and LDA. |
| [Capital One](https://www.capitalone.com/) |  | 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}
}
[citing gensim in academic papers and theses]: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=9vG_kV0AAAAJ&citation_for_view=9vG_kV0AAAAJ:NaGl4SEjCO4C
[design goals]: https://radimrehurek.com/gensim/intro.html#design-principles
[RaRe Technologies]: https://rare-technologies.com/wp-content/uploads/2016/02/rare_image_only.png%20=10x20
[rare\_tech]: //rare-technologies.com
[Talentpair]: https://avatars3.githubusercontent.com/u/8418395?v=3&s=100
[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
[documentation and Jupyter Notebook tutorials]: https://github.com/RaRe-Technologies/gensim/#documentation
[Vector Space Model]: https://en.wikipedia.org/wiki/Vector_space_model
[unsupervised document analysis]: https://en.wikipedia.org/wiki/Latent_semantic_indexing
[NumPy]: https://numpy.org/install/
[linking NumPy to a BLAS library]: https://numpy.org/devdocs/building/blas_lapack.html
[ATLAS]: https://math-atlas.sourceforge.net/
[OpenBLAS]: https://xianyi.github.io/OpenBLAS/
[source tar.gz]: https://pypi.org/project/gensim/
[documentation]: https://radimrehurek.com/gensim/#install