https://github.com/charliejharrison/gensim
Topic Modelling for Humans
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: scholar.google -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.7%) to scientific vocabulary
Last synced: 10 months ago
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Repository
Topic Modelling for Humans
Basic Info
- Host: GitHub
- Owner: charliejharrison
- License: lgpl-2.1
- Language: Python
- Default Branch: develop
- Homepage: http://radimrehurek.com/gensim/
- Size: 33 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of piskvorky/gensim
Created about 9 years ago
· Last pushed about 9 years ago
https://github.com/charliejharrison/gensim/blob/develop/
gensim Topic Modelling in Python
==================================
[](https://travis-ci.org/RaRe-Technologies/gensim)[]()[](https://pypi.python.org/pypi/gensim)
[](https://groups.google.com/forum/#!forum/gensim)
[](https://gitter.im/RaRe-Technologies/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.
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.
Support
------------
Please raise potential bugs on [github](https://github.com/RaRe-Technologies/gensim/blob/develop/CONTRIBUTING.md). See [Contribution Guide](https://github.com/RaRe-Technologies/gensim/blob/develop/CONTRIBUTING.md) prior to raising an issue.
If you have an open-ended or a research question:
- [Mailing List] is the best option
- [Gitter chat room] is also available
[Mailing List]: https://groups.google.com/forum/#!forum/gensim
[Gitter chat room]: https://gitter.im/RaRe-Technologies/gensim
Installation
------------
This software depends on [NumPy and Scipy], two Python packages for
scientific computing. You must have them installed prior to installing
gensim.
It is also recommended you install a fast BLAS library before installing
NumPy. This is optional, but using an optimized BLAS such as [ATLAS] or
[OpenBLAS] is known to improve performance by as much as an order of
magnitude. On OS X, NumPy picks up the BLAS that comes with it
automatically, so you dont need to do anything special.
The simple way to install gensim is:
pip install -U gensim
Or, if you have instead downloaded and unzipped the [source tar.gz]
package, youd run:
python setup.py test
python setup.py install
For alternative modes of installation (without root privileges,
development installation, optional install features), see the
[documentation].
This version has been tested under Python 2.7, 3.5 and 3.6. Gensims github repo is hooked
against [Travis CI for automated testing] on every commit push and pull
request. Support for Python 2.6, 3.3 and 3.4 was dropped in gensim 1.0.0. Install gensim 0.13.4 if you *must* use Python 2.6, 3.3 or 3.4. Support for Python 2.5 was dropped in gensim 0.10.0; install gensim 0.9.1 if you *must* use Python 2.5).
How come gensim is so fast and memory efficient? Isnt it pure Python, and isnt 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 Pythons built-in generators and
iterators for streamed data processing. Memory efficiency was one of
gensims [design goals], and is a central feature of gensim, rather than
something bolted on as an afterthought.
Documentation
-------------
- [QuickStart]
- [Tutorials]
- [Tutorial Videos]
- [Official API Documentation]
[QuickStart]: https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/gensim%20Quick%20Start.ipynb
[Tutorials]: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials
[Tutorial Videos]: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#videos
[Official Documentation and Walkthrough]: http://radimrehurek.com/gensim/
[Official API Documentation]: http://radimrehurek.com/gensim/apiref.html
---------
Adopters
--------
| Name | Logo | URL | Description |
|----------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| RaRe Technologies | 
| [rare-technologies.com](http://rare-technologies.com) | Machine learning & NLP consulting and training. Creators and maintainers of Gensim. |
| Mindseye | 
| [mindseye.com](http://www.mindseyesolutions.com/) | Similarities in legal documents |
| Talentpair |  | [talentpair.com](http://talentpair.com) | Data science driving high-touch recruiting |
| Tailwind | 
| [Tailwindapp.com](https://www.tailwindapp.com/)| Post interesting and relevant content to Pinterest |
| Issuu | 
| [Issuu.com](https://issuu.com/)| Gensims LDA module lies at the very core of the analysis we perform on each uploaded publication to figure out what its all about.
| Sports Authority | 
| [sportsauthority.com](https://en.wikipedia.org/wiki/Sports_Authority)| Text mining of customer surveys and social media sources |
| Search Metrics | 
| [searchmetrics.com](http://www.searchmetrics.com/)| Gensim word2vec used for entity disambiguation in Search Engine Optimisation
| Cisco Security | 
| [cisco.com](http://www.cisco.com/c/en/us/products/security/index.html)| Large-scale fraud detection
| 12K Research | 
| [12k.co](https://12k.co/)| Document similarity analysis on media articles
| National Institutes of Health | 
| [github/NIHOPA](https://github.com/NIHOPA/pipeline_word2vec)| Processing grants and publications with word2vec
| Codeq LLC | 
| [codeq.com](https://codeq.com)| Document classification with word2vec
| Mass Cognition | 
| [masscognition.com](http://www.masscognition.com/) | Topic analysis service for consumer text data and general text data |
| Stillwater Supercomputing | 
| [stillwater-sc.com](http://www.stillwater-sc.com/) | Document comprehension and association with word2vec |
| Channel 4 | 
| [channel4.com](http://www.channel4.com/) | Recommendation engine |
| Amazon | 
| [amazon.com](http://www.amazon.com/) | Document similarity|
| SiteGround Hosting | 
| [siteground.com](https://www.siteground.com/) | An ensemble search engine which uses different embeddings models and similarities, including word2vec, WMD, and LDA. |
-------
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
[Travis CI for automated testing]: https://travis-ci.org/RaRe-Technologies/gensim
[design goals]: http://radimrehurek.com/gensim/about.html
[RaRe Technologies]: http://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]: http://en.wikipedia.org/wiki/Vector_space_model
[unsupervised document analysis]: http://en.wikipedia.org/wiki/Latent_semantic_indexing
[NumPy and Scipy]: http://www.scipy.org/Download
[ATLAS]: http://math-atlas.sourceforge.net/
[OpenBLAS]: http://xianyi.github.io/OpenBLAS/
[source tar.gz]: http://pypi.python.org/pypi/gensim
[documentation]: http://radimrehurek.com/gensim/install.html
Owner
- Name: Charlie Harrison
- Login: charliejharrison
- Kind: user
- Repositories: 1
- Profile: https://github.com/charliejharrison