Gensim

Gensim

Radim Řehůřek
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+

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About

E5 Text Embeddings, developed by Microsoft, are advanced models designed to convert textual data into meaningful vector representations, enhancing tasks like semantic search and information retrieval. These models are trained using weakly-supervised contrastive learning on a vast dataset of over one billion text pairs, enabling them to capture intricate semantic relationships across multiple languages. The E5 family includes models of varying sizes—small, base, and large—offering a balance between computational efficiency and embedding quality. Additionally, multilingual versions of these models have been fine-tuned to support diverse languages, ensuring broad applicability in global contexts. Comprehensive evaluations demonstrate that E5 models achieve performance on par with state-of-the-art, English-only models of similar sizes.

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.

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

E5 Text Embeddings are designed for AI researchers, machine learning engineers, and developers seeking high-quality text representations for applications like semantic search, information retrieval, and multilingual NLP tasks

Audience

Machine learning practitioners seeking a solution for topic modeling and semantic analysis of large text corpora

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

No images available

Screenshots and Videos

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

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Microsoft
Founded: 1975
United States
github.com/microsoft/unilm/tree/master/e5

Company Information

Radim Řehůřek
Founded: 2009
Czech Republic
radimrehurek.com/gensim/

Alternatives

Alternatives

GloVe

GloVe

Stanford NLP
LexVec

LexVec

Alexandre Salle
word2vec

word2vec

Google
word2vec

word2vec

Google

Categories

Categories

Integrations

C
Cython
NumPy
Python
fastText
word2vec

Integrations

C
Cython
NumPy
Python
fastText
word2vec
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