gensim 0.8.6

Radim Rehurek — Sun, 09 Dec 2012 13:15:16 -0000

Gensim - Python Framework for Vector Space Modelling

Gensim is a Python library for Vector Space Modelling with very large corpora. Target audience is the Natural Language Processing (NLP) community.

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 or Random Projections
Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers.
extensive documentation and tutorials

Reference example

>>> from gensim import corpora, models, similarities
>>> 
>>> # load corpus iterator from a Matrix Market file on disk
>>> corpus = corpora.MmCorpus('/path/to/corpus.mm')
>>> 
>>> # initialize a transformation (Latent Semantic Indexing with 200 latent dimensions)
>>> lsi = models.LsiModel(corpus, num_topics=200)
>>> 
>>> # convert the same corpus to latent space and index it
>>> index = similarities.MatrixSimilarity(lsi[corpus])
>>> 
>>> # perform similarity query of another vector in LSI space against the whole corpus
>>> sims = index[query]

mloss.org gensim

gensim 0.8.6

Gensim - Python Framework for Vector Space Modelling

Features

Reference example