Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. This package provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.
You've just discovered text2vec!
text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP).
Goals which we aimed to achieve as a result of development of
The core functionality at the moment includes
Author of the package is a little bit obsessed about efficiency.
This package is efficient because it is carefully written in C++, which also means that text2vec is memory friendly. Some parts, such as training GloVe word embeddings, are fully parallelized using the excellent RcppParallel package. This means that the word embeddings are computed in parallel on OS X, Linux, Windows, and Solaris (x86) without any additional tuning or tricks. Other emrassingly parallel tasks such as vectorization can use any parallel backend wich supports foreach package. So they can achieve near-linear scalability with number of available cores. Finally, a streaming API means that users do not have to load all the data into RAM.
The package has issue tracker on GitHub where I'm filing feature requests and notes for future work. Any ideas are appreciated.
Contributors are welcome. You can help by:
GPL (>= 2)
2016-10-03. See 0.4 milestone tags.
doc_proportions. see #52.
prune_vocabulary. signature also was changed.
transform_*- more intuitive + simpler usage with autocompletion
itoken. Simplifies assignement of ids to rows of DTM
create_vocabularynow can handle
First CRAN release of text2vec.