Gensim

Gensim

Radim Řehůřek
+

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About

Gensim is a free, open source Python library designed for unsupervised topic modeling and natural language processing, focusing on large-scale semantic modeling. It enables the training of models like Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), facilitating the representation of documents as semantic vectors and the discovery of semantically related documents. Gensim is optimized for performance with highly efficient implementations in Python and Cython, allowing it to process arbitrarily large corpora using data streaming and incremental algorithms without loading the entire dataset into RAM. It is platform-independent, running on Linux, Windows, and macOS, and is licensed under the GNU LGPL, promoting both personal and commercial use. The library is widely adopted, with thousands of companies utilizing it daily, over 2,600 academic citations, and more than 1 million downloads per week.

About

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.

About

statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. An extensive list of result statistics is available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open-source Modified BSD (3-clause) license. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings. You can also use numpy arrays instead of formulas. The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. This is the recommended installation method for most users.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Machine learning practitioners seeking a solution for topic modeling and semantic analysis of large text corpora

Audience

Developers interested in a beautiful but advanced programming language

Audience

Users and anyone in search of a solution to calculate the estimation of many different statistical models

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Radim Řehůřek
Founded: 2009
Czech Republic
radimrehurek.com/gensim/

Company Information

Python
Founded: 1991
www.python.org

Company Information

statsmodels
www.statsmodels.org/stable/index.html

Alternatives

GloVe

GloVe

Stanford NLP

Alternatives

Alternatives

word2vec

word2vec

Google
Cohere

Cohere

Cohere AI

Categories

Categories

Categories

Integrations

AMD Developer Cloud
AWS ParallelCluster
Codeaid
CudaText
Droidrun
Duckly
EditRocket
Figstack
GPT-4o mini
Gemini
Google Cloud Run
Intel Tiber AI Studio
Kite
Mend.io
Modelscape
Nhost
Roost.ai
Steev
Tinify CDN
python-sql

Integrations

AMD Developer Cloud
AWS ParallelCluster
Codeaid
CudaText
Droidrun
Duckly
EditRocket
Figstack
GPT-4o mini
Gemini
Google Cloud Run
Intel Tiber AI Studio
Kite
Mend.io
Modelscape
Nhost
Roost.ai
Steev
Tinify CDN
python-sql

Integrations

AMD Developer Cloud
AWS ParallelCluster
Codeaid
CudaText
Droidrun
Duckly
EditRocket
Figstack
GPT-4o mini
Gemini
Google Cloud Run
Intel Tiber AI Studio
Kite
Mend.io
Modelscape
Nhost
Roost.ai
Steev
Tinify CDN
python-sql
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