Word Vectors is an R package for building and exploring word2vec and other word embedding models.
Key Features
- Trains word2vec models using an extended Jian Li’s word2vec code; reads and writes the binary word2vec format so that you can import pre-trained models such as Google’s; and provides tools for reading only part of a model (rows or columns) so you can explore a model in memory-limited situations.
- Creates a new VectorSpaceModel class in R that gives a better syntax for exploring a word2vec or GloVe model than native matrix methods.
- Implements several basic matrix operations that are useful in exploring word embedding models including cosine similarity, nearest neighbor, and vector projection with some caching that makes them much faster than the simplest implementations.
Website: github.com/bmschmidt/wordVectors
Support:
Developer: Benjamin Schmidt
License: Apache License Version 2.0
Word Vectors is written in R. Learn R with our recommended free books and free tutorials.
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