Gensim Description

Gensim is an open-source Python library that specializes in unsupervised topic modeling and natural language processing, with an emphasis on extensive semantic modeling. It supports the development of various models, including Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), which aids in converting documents into semantic vectors and in identifying documents that are semantically linked. With a strong focus on performance, Gensim features highly efficient implementations crafted in both Python and Cython, enabling it to handle extremely large corpora through the use of data streaming and incremental algorithms, which allows for processing without the need to load the entire dataset into memory. This library operates independently of the platform, functioning seamlessly on Linux, Windows, and macOS, and is distributed under the GNU LGPL license, making it accessible for both personal and commercial applications. Its popularity is evident, as it is employed by thousands of organizations on a daily basis, has received over 2,600 citations in academic works, and boasts more than 1 million downloads each week, showcasing its widespread impact and utility in the field. Researchers and developers alike have come to rely on Gensim for its robust features and ease of use.

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Pricing

Pricing Starts At:
Free
Free Version:
Yes

Integrations

API:
Yes, Gensim has an API
View Integrations

Reviews

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Company Details

Company:
Radim Řehůřek
Year Founded:
2009
Headquarters:
Czech Republic
Website:
radimrehurek.com/gensim/

Media

Gensim Screenshot 1
Gensim Screenshot 1
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Product Details

Platforms
Windows
Mac
Linux
Types of Training
Training Docs
Customer Support
Online Support

Gensim Features and Options

Natural Language Processing Software

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

Gensim User Reviews

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