fastTextR

UPDATE 11-04-2019: There is an updated version of the fastText R package which includes all the features of the ported fasttext library. Therefore this repository is archived.

The fastTextR package is an R wrapper (only) for the skipgram and cbow functions of the fastText library. fastText is a library for efficient learning of word representations and sentence classification. Since it uses C++11 features, it requires a compiler with good C++11 support. These include : (gcc-4.6.3 or newer) or (clang-3.3 or newer). More information about the fastText library can be found in https://github.com/facebookresearch/fastText. COPYRIGHTS, LICENSE and PATENTS files can be found in the inst folder of the R package.

A detailed example can be found in my blog-post about text processing, in section ‘word vectors’.

To install the package from Github you can either use the install_github function of the devtools package,

devtools::install_github('mlampros/fastTextR')

or directly download the fastTextR-zip file using the Clone or download button in the repository page, extract it locally (rename it to fastTextR if necessary and check that files such as DESCRIPTION, NAMESPACE etc. are present when you open the fastTextR folder) and then run,

#-------------
# on a Unix OS
#-------------

setwd('/your_folder/fastTextR/')
Rcpp::compileAttributes(verbose = TRUE)
setwd('/your_folder/')
system("R CMD build fastTextR")
system("R CMD INSTALL fastTextR_1.0.2.tar.gz")


#------------------
# on the Windows OS  
#------------------

setwd('C:/your_folder/fastTextR/')
Rcpp::compileAttributes(verbose = TRUE)
setwd('C:/your_folder/')
system("R CMD build fastTextR")
system("R CMD INSTALL fastTextR_1.0.2.tar.gz")

Use the following link to report bugs/issues (for the R wrapper),

https://github.com/mlampros/fastTextR/issues

Example usage

# example input data ---> 'dat.txt'



library(fastTextR)



#--------------------------
# skipgram or cbow methods
#--------------------------


res = skipgram_cbow(input_path = "/data_fasttext/dat.txt",

                    output_path = "/data_fasttext/model", 
                    
                    method = "skipgram", lr = 0.1, 
                    
                    lrUpdateRate = 100, dim = 100,
                    
                    ws = 5, epoch = 5, minCount = 1, 
                    
                    neg = 5, wordNgrams = 1, loss = "ns", 
                    
                    bucket = 2000000, minn = 0,
                    
                    maxn = 0, thread = 6, t = 0.0001, 
                    
                    verbose = 2)
                    
                    
                
#-------------------------------------------------------------
# prediction of unknown words for the skipgram and cbow models
#-------------------------------------------------------------


res = predict_unknown_words(skipgram_cbow_model_output = "/data_fasttext/model.bin",

                            unknown_words_path = "/data_fasttext/queries.txt",
                            
                            output_path = "/data_fasttext/NEW_VEC",
                            
                            verbose = TRUE)

More information about the parameters of each function can be found in the package documentation.