Skip to main content

Python framework for fast Vector Space Modelling

Project description

Travis

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

For a Python3 port of gensim by Parikshit Samant, visit this fork.

Features

  • All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM),

  • Intuitive interfaces

    • easy to plug in your own input corpus/datastream (trivial streaming API)

    • easy to extend with other Vector Space algorithms (trivial transformation API)

  • Efficient implementations of popular algorithms, such as online Latent Semantic Analysis, Latent Dirichlet Allocation, Random Projections or word2vec deep learning.

  • Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers, and word2vec on multiple cores.

  • Extensive HTML documentation and tutorials.

If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia.

Installation

This software depends on NumPy and Scipy, two Python packages for scientific computing. You must have them installed prior to installing gensim.

It is also recommended you install a fast BLAS library prior to installing NumPy. This is optional, but using an optimized BLAS such as ATLAS or OpenBLAS is known to improve performance by as much as an order of magnitude.

The simple way to install gensim is:

sudo easy_install gensim

Or, if you have instead downloaded and unzipped the source tar.gz package, you’ll need to run:

python setup.py test
sudo python setup.py install

For alternative modes of installation (without root privileges, development installation, optional install features), see the documentation.

This version has been tested under Python 2.5, 2.6 and 2.7, and should run on any 2.5 <= Python < 3.0.

Documentation

Manual for the gensim package is available in HTML. It contains a walk-through of all its features and a complete reference section. It is also included in the source distribution package.


Gensim is open source software released under the GNU LGPL license. Copyright (c) 2009-2013 Radim Rehurek

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gensim-0.8.9.tar.gz (2.8 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gensim-0.8.9-py2.py3-none-any.whl (2.1 MB view details)

Uploaded Python 2Python 3

File details

Details for the file gensim-0.8.9.tar.gz.

File metadata

  • Download URL: gensim-0.8.9.tar.gz
  • Upload date:
  • Size: 2.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for gensim-0.8.9.tar.gz
Algorithm Hash digest
SHA256 dbbe889dea62141f0d5e0397cf0a82ba776266c372607c7377da2906fd1e5c99
MD5 8437e497aebfa365d8c26bbd600e2c1f
BLAKE2b-256 4d665f97d2c4bedd0a55736fff38445698ee6c544dd466b8c6160b88bfcf0806

See more details on using hashes here.

File details

Details for the file gensim-0.8.9-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for gensim-0.8.9-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 61ddc3afdcc8d5c4aafccf407f25f146e26619e884bb230ed12dcbd04f9fd346
MD5 d20765308d51104032735acd13274b1c
BLAKE2b-256 9a5a3a796c9ac9aeaba0dd65bbc8b71e83b37ea4fb270bc0fbc0554a159e6cb6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page