Gensim

Gensim

Radim Řehůřek
word2vec

word2vec

Google
+

Related Products

  • Vertex AI
    743 Ratings
    Visit Website
  • RunPod
    180 Ratings
    Visit Website
  • AnalyticsCreator
    46 Ratings
    Visit Website
  • LM-Kit.NET
    22 Ratings
    Visit Website
  • Enterprise Bot
    23 Ratings
    Visit Website
  • Cloudflare
    1,882 Ratings
    Visit Website
  • Google AI Studio
    9 Ratings
    Visit Website
  • kama DEI
    8 Ratings
    Visit Website
  • Quaeris
    6 Ratings
    Visit Website
  • UnForm
    18 Ratings
    Visit Website

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

Word2Vec is a neural network-based technique for learning word embeddings, developed by researchers at Google. It transforms words into continuous vector representations in a multi-dimensional space, capturing semantic relationships based on context. Word2Vec uses two main architectures: Skip-gram, which predicts surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which predicts a target word based on surrounding words. By training on large text corpora, Word2Vec generates word embeddings where similar words are positioned closely, enabling tasks like semantic similarity, analogy solving, and text clustering. The model was influential in advancing NLP by introducing efficient training techniques such as hierarchical softmax and negative sampling. Though newer embedding models like BERT and Transformer-based methods have surpassed it in complexity and performance, Word2Vec remains a foundational method in natural language processing and machine learning research.

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

Researchers, data scientists, and developers working in natural language processing (NLP) and machine learning who need efficient word embeddings for text analysis and semantic understanding

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

No images available

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:

Review this Software

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

Google
Founded: 1998
United States
code.google.com/archive/p/word2vec/

Alternatives

GloVe

GloVe

Stanford NLP

Alternatives

Alternatives

word2vec

word2vec

Google
Gensim

Gensim

Radim Řehůřek
GloVe

GloVe

Stanford NLP
Cohere

Cohere

Cohere AI
LexVec

LexVec

Alexandre Salle

Categories

Categories

Categories

Integrations

Actian Ingres
ApiFlash
Boost Note
Cenchu
CodePeer
Codegrip
Cosine Genie
Cython
DebuggAI
DevCycle
Equip
Extism
Google Cloud Run
Queue-Fair
TDX360
TensorFlow
Tobiko
Tornado Web Server
finlight.me
unittest

Integrations

Actian Ingres
ApiFlash
Boost Note
Cenchu
CodePeer
Codegrip
Cosine Genie
Cython
DebuggAI
DevCycle
Equip
Extism
Google Cloud Run
Queue-Fair
TDX360
TensorFlow
Tobiko
Tornado Web Server
finlight.me
unittest

Integrations

Actian Ingres
ApiFlash
Boost Note
Cenchu
CodePeer
Codegrip
Cosine Genie
Cython
DebuggAI
DevCycle
Equip
Extism
Google Cloud Run
Queue-Fair
TDX360
TensorFlow
Tobiko
Tornado Web Server
finlight.me
unittest
Claim Gensim and update features and information
Claim Gensim and update features and information
Claim Python and update features and information
Claim Python and update features and information
Claim word2vec and update features and information
Claim word2vec and update features and information