Gensim

Gensim

Radim Řehůřek
VBScript

VBScript

Microsoft
+

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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

VBScript is a programming language included with Microsoft Internet Explorer. For other browsers, contact your vendor about support. VBScript 2.0 (or later) is recommended for use with Agent. Although earlier versions of VBScript may work with Agent, they lack certain functions that you may want to use. You can download VBScript 2.0 and obtain further information on VBScript at the Microsoft Downloads site and the Microsoft VBScript site. With VBScript (2.0 or later), you can verify whether Microsoft Agent is installed by trying to create the object and checking to see if it exists. The following sample demonstrates how to check for the Agent control without triggering an auto-download of the control.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

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

Programming Language solution for developers

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

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

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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

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

Microsoft
docs.microsoft.com/en-us/windows/win32/lwef/using-vbscript

Company Information

scikit-learn
United States
scikit-learn.org/stable/

Alternatives

GloVe

GloVe

Stanford NLP

Alternatives

PDF Junction

PDF Junction

Aquaforest

Alternatives

Gensim

Gensim

Radim Řehůřek
word2vec

word2vec

Google
ML.NET

ML.NET

Microsoft
MLlib

MLlib

Apache Software Foundation
Cohere

Cohere

Cohere AI
Viscosity

Viscosity

SparkLabs
Keepsake

Keepsake

Replicate

Categories

Categories

Categories

Integrations

Advanced Email Parser
C
CodePal
Flower
Guild AI
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Matplotlib
ModelOp
NumPy
Overleaf
Python
SlickEdit
Train in Data
Typora
fastText
phase 5

Integrations

Advanced Email Parser
C
CodePal
Flower
Guild AI
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Matplotlib
ModelOp
NumPy
Overleaf
Python
SlickEdit
Train in Data
Typora
fastText
phase 5

Integrations

Advanced Email Parser
C
CodePal
Flower
Guild AI
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Matplotlib
ModelOp
NumPy
Overleaf
Python
SlickEdit
Train in Data
Typora
fastText
phase 5
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