Gensim

Gensim

Radim Řehůřek
GloVe

GloVe

Stanford NLP

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

GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm developed by the Stanford NLP Group to obtain vector representations for words. It constructs word embeddings by analyzing global word-word co-occurrence statistics from a given corpus, resulting in vector spaces where the geometric relationships reflect semantic similarities and differences among words. A notable feature of GloVe is its ability to capture linear substructures within the word vector space, enabling vector arithmetic to express relationships. The model is trained on the non-zero entries of a global word-word co-occurrence matrix, which records how frequently pairs of words appear together in a corpus. This approach efficiently leverages statistical information by focusing on significant co-occurrences, leading to meaningful word representations. Pre-trained word vectors are available for various corpora, including Wikipedia 2014.

About

IronPython is an open-source implementation of the Python programming language which is tightly integrated with .NET. IronPython can use .NET and Python libraries, and other .NET languages can use Python code just as easily. Experience a more interactive .NET and Python development experience with Python Tools for Visual Studio. IronPython is an excellent addition to .NET, providing Python developers with the power of the .NET. Existing .NET developers can also use IronPython as a fast and expressive scripting language for embedding, testing, or writing a new application from scratch. The CLR is a great platform for creating programming languages, and the DLR makes it all the better for dynamic languages. Also, the .NET (base class library, presentation foundation, etc.) gives developers an amazing amount of functionality and power. IronPython uses Python syntax and standard libraries and so your Python code will need to be updated accordingly.

About

Vyper is a contract-oriented, pythonic programming language that targets the Ethereum Virtual Machine (EVM). Security: It should be possible and natural to build secure smart-contracts in Vyper. Language and compiler simplicity: The language and the compiler implementation should strive to be simple. Auditability: Vyper code should be maximally human-readable. Furthermore, it should be maximally difficult to write misleading code. Simplicity for the reader is more important than simplicity for the writer, and simplicity for readers with low prior experience with Vyper (and low prior experience with programming in general) is particularly important.

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

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

Data scientists in search of a solution to enhance their natural language processing models with word embeddings that capture global statistical information from large text corpora

Audience

Developers requiring a scripting language for embedding, testing, or writing new applications

Audience

Smart contract developers

Support

Phone Support
24/7 Live Support
Online

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

API

Offers API

Screenshots and Videos

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

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

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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

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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

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Training

Documentation
Webinars
Live Online
In Person

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

Stanford NLP
United States
nlp.stanford.edu/projects/glove/

Company Information

IronPython
ironpython.net

Company Information

Vyper
Founded: 2017
docs.vyperlang.org/en/stable/

Alternatives

GloVe

GloVe

Stanford NLP

Alternatives

Alternatives

Alternatives

Clarity

Clarity

Clarity Smart Contracts
word2vec

word2vec

Google
Gensim

Gensim

Radim Řehůřek
LexVec

LexVec

Alexandre Salle
Cohere

Cohere

Cohere AI
word2vec

word2vec

Google
Mojo

Mojo

Modular
Odin

Odin

Odin Language

Categories

Categories

Categories

Categories

Integrations

C
Cython
Ethereum
Neon EVM
NumPy
Python
Visual Studio
fastText
word2vec

Integrations

C
Cython
Ethereum
Neon EVM
NumPy
Python
Visual Studio
fastText
word2vec

Integrations

C
Cython
Ethereum
Neon EVM
NumPy
Python
Visual Studio
fastText
word2vec

Integrations

C
Cython
Ethereum
Neon EVM
NumPy
Python
Visual Studio
fastText
word2vec
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