gensim · PyPI

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Gensim is a Python framework for unsupervised learning from raw, unstructured digital texts. It provides a framework for learning hidden (latent) corpus structure. Once found, documents can be succinctly expressed in terms of this structure, queried for topical similarity and so on.

Gensim includes the following features:

Memory independence – there is no need for the whole text corpus (or any intermediate term-document matrices) to reside fully in RAM at any one time.
Provides implementations for several popular topic inference algorithms, including Latent Semantic Analysis (LSA, LSI) and Latent Dirichlet Allocation (LDA), and makes adding new ones simple.
Contains I/O wrappers and converters around several popular data formats.
Allows similarity queries across documents in their latent, topical representation.

The principal design objectives behind gensim are:

Straightforward interfaces and low API learning curve for developers, facilitating modifications and rapid prototyping.
Memory independence with respect to the size of the input corpus; all intermediate steps and algorithms operate in a streaming fashion, processing one document at a time.

Metadata

Release files for gensim 0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for gensim 0.2
File	Size	Uploaded
gensim-0.2.tar.gz	119.1 kB	Mar 18, 2010	Details

Built distribution (wheel)

Table of built distributions (wheels) for gensim 0.2
File	Interpreter	ABI	Platform	Reset
gensim-0.2-py2.5.egg 130.7 kB Mar 18, 2010	Legacy Egg format	-	-	Details

Total release size: 249.8 kB

Release files / gensim-0.2.tar.gz

Download URL	gensim-0.2.tar.gz
Size	119.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`df51d7a3e254e0d6e7d3998d46bd0da6f13352106594985b9bd277b000838d1b`
BLAKE2b-256 checksum How to use checksums	`b4c74813ac45df446d4fa92575e8c863cdae68a79d11775caafcda3cdd665904`
Upload date	Mar 18, 2010
Uploaded using Trusted Publishing? What is trusted publishing?	No

Release files / gensim-0.2-py2.5.egg

Download URL	gensim-0.2-py2.5.egg
Size	130.7 kB
Tags	Egg
SHA-256 checksum How to use checksums	`86aae1eab2409436b1a7790d402a7c6e816adda98082b22c69a61ff06d2147da`
BLAKE2b-256 checksum How to use checksums	`496980e1aa54ff72384ae13a4501d819d8eeb13e7fd6d36aad595998b22979ce`
Upload date	Mar 18, 2010
Uploaded using Trusted Publishing? What is trusted publishing?	No

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This release

0.2 This release

Mar 18, 2010 2 release files

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