https://github.com/dselivanov/text2vec

Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
https://github.com/dselivanov/text2vec

glove latent-dirichlet-allocation natural-language-processing text-mining topic-modeling vectorization word-embeddings word2vec

Last synced: 13 days ago
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Fast vectorization, topic modeling, distances and GloVe word embeddings in R.

• Host: GitHub
• URL: https://github.com/dselivanov/text2vec
• Owner: dselivanov
• License: other
• Created: 2015-08-05T06:09:14.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2024-08-16T02:54:30.000Z (8 months ago)
• Last Synced: 2025-04-06T08:34:23.581Z (15 days ago)
• Topics: glove, latent-dirichlet-allocation, natural-language-processing, text-mining, topic-modeling, vectorization, word-embeddings, word2vec
• Language: R
• Homepage: http://text2vec.org
• Size: 46.2 MB
• Stars: 860
• Watchers: 53
• Forks: 133
• Open Issues: 25
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-nlp - text2vec - R 中的快速矢量化，主題建模，距離和 GloVe 字嵌入。 (函式庫 / 書籍)

README

        
**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 `text2vec`:

* **Concise** - expose as few functions as possible

* **Consistent** - expose unified interfaces, no need to explore new interface for each task

* **Flexible** - allow to easily solve complex tasks

* **Fast** - maximize efficiency per single thread, transparently scale to multiple threads on multicore machines

* **Memory efficient** - use streams and iterators, not keep data in RAM if possible

See [API](http://text2vec.org/api.html) section for details.

# Performance

![htop](http://text2vec.org/images/htop.png)

This package is efficient because it is carefully written in C++, which also means that text2vec is memory friendly. Some parts are fully parallelized using OpenMP. 

Other emrassingly parallel tasks (such as vectorization) can use any fork-based parallel backend on UNIX-like machines. They can achieve near-linear scalability with the number of available cores. 

Finally, a streaming API means that  users do not have to load all the data into RAM. 

# Contributing

The package has [issue tracker on GitHub](https://github.com/dselivanov/text2vec/issues) where I'm filing feature requests and notes for future work. Any ideas are appreciated.

Contributors are welcome. You can help by: 

- testing and leaving feedback on the [GitHub issuer tracker](https://github.com/dselivanov/text2vec/issues) (preferably) or directly by e-mail

- forking and contributing (check [code our style guide](https://github.com/dselivanov/text2vec/wiki/Code-style-guide)). Vignettes, docs, tests, and use cases are very welcome

- by giving me a star on [project page](https://github.com/dselivanov/text2vec) :-)

# License

GPL (>= 2)