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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.
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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.
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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.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Machine learning practitioners seeking a solution for topic modeling and semantic analysis of large text corpora
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Audience
Developers interested in a beautiful but advanced programming language
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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
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and VideosNo images available
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Pricing
Free
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationRadim Řehůřek
Founded: 2009
Czech Republic
radimrehurek.com/gensim/
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Company InformationPython
Founded: 1991
www.python.org
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Company InformationGoogle
Founded: 1998
United States
code.google.com/archive/p/word2vec/
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Integrations
Actian Ingres
ApiFlash
Boost Note
Cenchu
CodePeer
Codegrip
Cosine Genie
Cython
DebuggAI
DevCycle
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Integrations
Actian Ingres
ApiFlash
Boost Note
Cenchu
CodePeer
Codegrip
Cosine Genie
Cython
DebuggAI
DevCycle
|
Integrations
Actian Ingres
ApiFlash
Boost Note
Cenchu
CodePeer
Codegrip
Cosine Genie
Cython
DebuggAI
DevCycle
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