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:
1. Straightforward interfaces and low API learning curve for developers,
facilitating modifications and rapid prototyping.
2. 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.
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:
1. Straightforward interfaces and low API learning curve for developers,
facilitating modifications and rapid prototyping.
2. 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.
Release files for gensim 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gensim-0.3.0.tar.gz | 124.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gensim-0.3.0-py2.5.egg | Legacy Egg format | - | - | Details |
Total release size: 255.3 kB
Release files / gensim-0.3.0.tar.gz
| Download URL | gensim-0.3.0.tar.gz |
|---|---|
| Size | 124.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
9a43e91473b3b7b8471e1ac5d3a2df1615f6d14a343de4bf088504787c24a3b5
|
|
BLAKE2b-256 checksum How to use checksums |
0eb23c062f4009408209bb899dceb9093d814c76d5fec141136ae5f9075e9e81
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Release files / gensim-0.3.0-py2.5.egg
| Download URL | gensim-0.3.0-py2.5.egg |
|---|---|
| Size | 130.8 kB |
| Tags | Egg |
|
SHA-256 checksum How to use checksums |
6baeb63d82020307ef43a777f91730230d1f9304a0dc6b0433771f4026be9ff6
|
|
BLAKE2b-256 checksum How to use checksums |
526688ec78bc4ea8aa2318ccf44f9fd2349ebfef75e6ab37fdea8b7974b7696e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
