| Type: | Package |
| Title: | Efficient Learning of Word Representations and Sentence Classification |
| Version: | 1.0.4 |
| Date: | 2024-02-17 |
| URL: | https://github.com/mlampros/fastText |
| BugReports: | https://github.com/mlampros/fastText/issues |
| Description: | An interface to the 'fastText' https://github.com/facebookresearch/fastText library for efficient learning of word representations and sentence classification. The 'fastText' algorithm is explained in detail in (i) "Enriching Word Vectors with subword Information", Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov, 2017, <doi:10.1162/tacl_a_00051>; (ii) "Bag of Tricks for Efficient Text Classification", Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov, 2017, <doi:10.18653/v1/e17-2068>; (iii) "FastText.zip: Compressing text classification models", Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou, Tomas Mikolov, 2016, <doi:10.48550/arXiv.1612.03651>. |
| License: | MIT + file LICENSE |
| SystemRequirements: | Generally, fastText builds on modern Mac OS and Linux distributions. Since it uses some C++11 features, it requires a compiler with good C++11 support. These include a (g++-4.7.2 or newer) or a (clang-3.3 or newer). |
| Encoding: | UTF-8 |
| Imports: | Rcpp (≥ 1.0.0), ggplot2, grid, utils, glue, data.table, stats |
| Depends: | R(≥ 3.2.3) |
| LinkingTo: | Rcpp |
| Suggests: | testthat, covr, knitr, rmarkdown |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.3.0 |
| NeedsCompilation: | yes |
| Packaged: | 2024-02-17 17:39:42 UTC; lampros |
| Author: | Lampros Mouselimis
 [aut, cre],
Facebook Inc [cph] |
| Maintainer: | Lampros Mouselimis <mouselimislampros@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-02-17 18:00:02 UTC |
elapsed time in hours & minutes & seconds
Description
elapsed time in hours & minutes & seconds
Usage
compute_elapsed_time(time_start)
Arguments
time_start |
a numeric value specifying the start time |
Value
It does not return a value but only prints the time in form of a character string in the R session
Interface for the fasttext library
Description
Interface for the fasttext library
Usage
fasttext_interface(
list_params,
path_output = "",
MilliSecs = 100,
path_input = "",
remove_previous_file = TRUE,
print_process_time = FALSE
)
Arguments
list_params |
a list of valid parameters |
path_output |
a character string specifying the file path where the process-logs (or output in generally) should be saved |
MilliSecs |
an integer specifying the delay in milliseconds when printing the results to the specified path_output |
path_input |
a character string specifying the path to the input data file |
remove_previous_file |
a boolean. If TRUE, in case that the path_output is not an empty string (""), then an existing file with the same output name will be removed |
print_process_time |
a boolean. If TRUE then the processing time of the function will be printed out in the R session |
Details
This function allows the user to run the various methods included in the fasttext library from within R
The "output" parameter which exists in the named list (see examples section) and is passed to the "list_params" parameter of the "fasttext_interface()" function, is a file path and not a directory name and will actually return two files (a *.vec* and a *.bin*) to the output directory.
Value
a vector of class character that includes the parameters and file paths used as input to the function
References
https://github.com/facebookresearch/fastText
https://github.com/facebookresearch/fastText/blob/master/docs/supervised-tutorial.md
Examples
## Not run:
library(fastText)
####################################################################################
# If the user intends to run the following examples then he / she must replace #
# the 'input', 'output', 'path_input', 'path_output', 'model' and 'test_data' file #
# paths depending on where the data are located or should be saved! #
# ( 'tempdir()' is used here as an example folder ) #
####################################################################################
# ------------------------------------------------
# print information for the Usage of each function [ parameters ]
# ------------------------------------------------
fastText::printUsage()
fastText::printTestUsage()
fastText::printTestLabelUsage()
fastText::printQuantizeUsage()
fastText::printPrintWordVectorsUsage()
fastText::printPrintSentenceVectorsUsage()
fastText::printPrintNgramsUsage()
fastText::printPredictUsage()
fastText::printNNUsage()
fastText::printDumpUsage()
fastText::printAnalogiesUsage()
fastText::print_parameters(command = "supervised")
# -----------------------------------------------------------------------
# In case that the 'command' is one of 'cbow', 'skipgram' or 'supervised'
# -----------------------------------------------------------------------
list_params = list(command = 'cbow',
lr = 0.1,
dim = 200,
input = file.path(tempdir(), "doc.txt"),
output = tempdir(),
verbose = 2,
thread = 1)
res = fasttext_interface(list_params,
path_output = file.path(tempdir(),"model_logs.txt"),
MilliSecs = 100)
# ---------------------
# 'supervised' training
# ---------------------
list_params = list(command = 'supervised',
lr = 0.1,
dim = 200,
input = file.path(tempdir(), "cooking.train"),
output = file.path(tempdir(), "model_cooking"),
verbose = 2,
thread = 1)
res = fasttext_interface(list_params,
path_output = file.path(tempdir(), 'logs_supervise.txt'),
MilliSecs = 5)
# ---------------------------------------
# In case that the 'command' is 'predict'
# ---------------------------------------
list_params = list(command = 'predict',
model = file.path(tempdir(), 'model_cooking.bin'),
test_data = file.path(tempdir(), 'cooking.valid'),
k = 1,
th = 0.0)
res = fasttext_interface(list_params,
path_output = file.path(tempdir(), 'predict_valid.txt'))
# ------------------------------------
# In case that the 'command' is 'test' [ k = 5 , means that precision and recall are at 5 ]
# ------------------------------------
list_params = list(command = 'test',
model = file.path(tempdir(), 'model_cooking.bin'),
test_data = file.path(tempdir(), 'cooking.valid'),
k = 5,
th = 0.0)
res = fasttext_interface(list_params) # It only prints 'Precision', 'Recall' to the R session
# ------------------------------------------
# In case that the 'command' is 'test-label' [ k = 5 , means that precision and recall are at 5 ]
# ------------------------------------------
list_params = list(command = 'test-label',
model = file.path(tempdir(), 'model_cooking.bin'),
test_data = file.path(tempdir(), 'cooking.valid'),
k = 5,
th = 0.0)
res = fasttext_interface(list_params, # prints also 'Precision', 'Recall' to R session
path_output = file.path(tempdir(), "test_valid.txt"))
# -----------------
# quantize function [ it will take a .bin file and return an .ftz file ]
# -----------------
# the quantize function is currenlty (01/02/2019) single-threaded
# https://github.com/facebookresearch/fastText/issues/353#issuecomment-342501742
list_params = list(command = 'quantize',
input = file.path(tempdir(), 'model_cooking.bin'),
output = file.path(tempdir(), gsub('.bin', '.ftz', 'model_cooking.bin')))
res = fasttext_interface(list_params)
# -----------------
# quantize function [ by using the optional parameters 'qnorm' and 'qout' ]
# -----------------
list_params = list(command = 'quantize',
input = file.path(tempdir(), 'model_cooking.bin'),
output = file.path(tempdir(), gsub('.bin', '.ftz', 'model_cooking.bin')),
qnorm = TRUE,
qout = TRUE)
res = fasttext_interface(list_params)
# ------------------
# print-word-vectors [ each line of the 'queries.txt' must be a single word ]
# ------------------
list_params = list(command = 'print-word-vectors',
model = file.path(tempdir(), 'model_cooking.bin'))
res = fasttext_interface(list_params,
path_input = file.path(tempdir(), 'queries.txt'),
path_output = file.path(tempdir(), 'print_vecs_file.txt'))
# ----------------------
# print-sentence-vectors [ See also the comments in the main.cc file about the input-file ]
# ----------------------
list_params = list(command = 'print-sentence-vectors',
model = file.path(tempdir(), 'model_cooking.bin'))
res = fasttext_interface(list_params,
path_input = file.path(tempdir(), 'text.txt'),
path_output = file.path(tempdir(), 'SENTENCE_VECs.txt'))
# ------------
# print-ngrams [ print to console or to output-file ]
# ------------
list_params = list(command = 'skipgram', lr = 0.1, dim = 200,
input = file.path(tempdir(), "doc.txt"),
output = tempdir(), verbose = 2, thread = 1,
minn = 2, maxn = 2)
res = fasttext_interface(list_params,
path_output = file.path(tempdir(), "ngram_out.txt"),
MilliSecs = 5)
list_params = list(command = 'print-ngrams',
model = file.path(tempdir(), 'ngram_out.bin'),
word = 'word') # print n-grams for specific word
res = fasttext_interface(list_params, path_output = "") # print output to console
res = fasttext_interface(list_params,
path_output = file.path(tempdir(), "NGRAMS.txt")) # output to file
# -------------
# 'nn' function
# -------------
list_params = list(command = 'nn',
model = file.path(tempdir(), 'model_cooking.bin'),
k = 20,
query_word = 'word') # a 'query_word' is required
res = fasttext_interface(list_params,
path_output = file.path(tempdir(), "nn_output.txt"))
# ---------
# analogies [ in the output file each analogy-triplet-result is separated with a newline ]
# ---------
list_params = list(command = 'analogies',
model = file.path(tempdir(), 'model_cooking.bin'),
k = 5)
res = fasttext_interface(list_params,
path_input = file.path(tempdir(), 'analogy_queries.txt'),
path_output = file.path(tempdir(), 'analogies_output.txt'))
# -------------
# dump function [ the 'option' param should be one of 'args', 'dict', 'input' or 'output' ]
# -------------
list_params = list(command = 'dump',
model = file.path(tempdir(), 'model_cooking.bin'),
option = 'args')
res = fasttext_interface(list_params,
path_output = file.path(tempdir(), "DUMP.txt"))
## End(Not run)
The Rcpp function which is used in the 'fasttext_interface' R function
Description
The Rcpp function which is used in the 'fasttext_interface' R function
Usage
give_args_fasttext(
args,
pth = "",
MilliSecs = 100L,
pth_in = "",
queryWord = "",
remove_previous_file = TRUE
)
Arguments
args |
the arguments that will be passed to the function in form of a character vector |
pth |
a character string specifying the path where the process-logs (or output in generally) should be saved |
MilliSecs |
an integer specifying the delay in milliseconds when printing the results to the specified path_output |
pth_in |
a character string specifying the path to the input data file |
queryWord |
either an empty string or the queryword that should be passed to the function |
remove_previous_file |
a boolean. If TRUE, in case that the path_output is not an empty string (""), then an existing file with the same output name will be removed |
Value
It does not return a value but only saves the results to a file
inner function of 'compute_elapsed_time'
Description
inner function of 'compute_elapsed_time'
Usage
inner_elapsed_time(secs, estimated = FALSE)
Arguments
secs |
a numeric value specifying the seconds |
estimated |
a boolean. If TRUE then the output label becomes the 'Estimated time' |
Value
a character string showing the estimated or elapsed time
Language Identification using fastText
Description
Language Identification using fastText
Usage
language_identification(
input_obj,
pre_trained_language_model_path,
k = 1,
th = 0,
threads = 1,
verbose = FALSE
)
Arguments
input_obj |
either a valid character string to a valid path where each line represents a different text extract or a vector of text extracts |
pre_trained_language_model_path |
a valid character string to the pre-trained language identification model path, for more info see https://fasttext.cc/docs/en/language-identification.html |
k |
predict top k labels (1 by default) |
th |
probability threshold (0.0 by default) |
threads |
an integer specifying the number of threads to run in parallel. This parameter applies only if k > 1 |
verbose |
if TRUE then information will be printed out in the console |
Value
an object of class data.table which includes two or more columns with the names 'iso_lang_N' and 'prob_N' where 'N' corresponds to 1 to 'k' input parameter
References
https://fasttext.cc/docs/en/language-identification.html https://becominghuman.ai/a-handy-pre-trained-model-for-language-identification-cadd89db9db8
Examples
library(fastText)
vec_txt = c("Incapaz de distinguir la luna y la cara de esta chica,
Las estrellas se ponen nerviosas en el cielo",
"Unable to tell apart the moon and this girl's face,
Stars are flustered up in the sky.")
file_pretrained = system.file("language_identification/lid.176.ftz", package = "fastText")
dtbl_out = language_identification(input_obj = vec_txt,
pre_trained_language_model_path = file_pretrained,
k = 3,
th = 0.0,
verbose = TRUE)
dtbl_out
Multiple plot function
Description
Multiple plot function
Usage
multiplot(..., plotlist = NULL, cols = 1, layout = NULL)
Arguments
... |
ellipsis to pass ggplot objects |
plotlist |
either NULL or a list of ggplot objects |
cols |
Number of columns in layout |
layout |
A matrix specifying the layout. If present, 'cols' is ignored |
Details
If the layout is something like matrix(c(1,2,3,3), nrow = 2, byrow = TRUE), then plot 1 will go in the upper left, 2 will go in the upper right, and 3 will go all the way across the bottom.
Value
It does not return a value but only shows the ggplots in the R session
References
http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/
Plot the progress of loss, learning-rate and word-counts
Description
Plot the progress of loss, learning-rate and word-counts
Usage
plot_progress_logs(path_logs = "progress_data.txt", plot = FALSE)
Arguments
path_logs |
a character string specifying a valid path to a file where the progress-logs are saved |
plot |
a boolean specifying if the loss, learning-rate and word-counts should be plotted |
Value
an object of class data.frame that includes the progress logs with columns 'progress', 'words_sec_thread', 'learning_rate' and 'loss'
References
http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/
Examples
## Not run:
library(fastText)
#-----------------------------------------------------------------
# the 'progress_data.txt' file corresponds to the 'path_output'
# parameter of the 'fasttext_interface()'. Therefore the user has
# to run first the 'fasttext_interface()' function to save the
# 'progress_data.txt' file to the desired folder.
#-----------------------------------------------------------------
res = plot_progress_logs(path = file.path(tempdir(), "progress_data.txt"),
plot = TRUE)
## End(Not run)
Print Usage Information when the command equals to 'analogies'
Description
Print Usage Information when the command equals to 'analogies'
Usage
printAnalogiesUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printAnalogiesUsage' function in the R session
Examples
library(fastText)
printAnalogiesUsage()
Print Usage Information when the command equals to 'dump'
Description
Print Usage Information when the command equals to 'dump'
Usage
printDumpUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printDumpUsage' function in the R session
Examples
library(fastText)
printDumpUsage()
Print Usage Information when the command equals to 'nn'
Description
Print Usage Information when the command equals to 'nn'
Usage
printNNUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printNNUsage' function in the R session
Examples
library(fastText)
printNNUsage()
Print Usage Information when the command equals to 'predict' or 'predict-prob'
Description
Print Usage Information when the command equals to 'predict' or 'predict-prob'
Usage
printPredictUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printPredictUsage' function in the R session
Examples
library(fastText)
printPredictUsage()
Print Usage Information when the command equals to 'print-ngrams'
Description
Print Usage Information when the command equals to 'print-ngrams'
Usage
printPrintNgramsUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printPrintNgramsUsage' function in the R session
Examples
library(fastText)
printPrintNgramsUsage()
Print Usage Information when the command equals to 'print-sentence-vectors'
Description
Print Usage Information when the command equals to 'print-sentence-vectors'
Usage
printPrintSentenceVectorsUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printPrintSentenceVectorsUsage' function in the R session
Examples
library(fastText)
printPrintSentenceVectorsUsage()
Print Usage Information when the command equals to 'print-word-vectors'
Description
Print Usage Information when the command equals to 'print-word-vectors'
Usage
printPrintWordVectorsUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printPrintWordVectorsUsage' function in the R session
Examples
library(fastText)
printPrintWordVectorsUsage()
Print Usage Information when the command equals to 'quantize'
Description
Print Usage Information when the command equals to 'quantize'
Usage
printQuantizeUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printQuantizeUsage' function in the R session
Examples
library(fastText)
printQuantizeUsage()
Print Usage Information when the command equals to 'test-label'
Description
Print Usage Information when the command equals to 'test-label'
Usage
printTestLabelUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printTestLabelUsage' function in the R session
Examples
library(fastText)
printTestLabelUsage()
Print Usage Information when the command equals to 'test'
Description
Print Usage Information when the command equals to 'test'
Usage
printTestUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printTestUsage' function in the R session
Examples
library(fastText)
printTestUsage()
Print Usage Information for all parameters
Description
Print Usage Information for all parameters
Usage
printUsage(verbose = TRUE)
Arguments
verbose |
if TRUE then information will be printed in the console |
Value
It does not return a value but only prints the available parameters of the 'printUsage' function in the R session
Examples
library(fastText)
printUsage()
Print the parameters for a specific command
Description
Print the parameters for a specific command
Usage
print_parameters(command = "supervised")
Arguments
command |
a character string specifying the command for which the parameters should be printed in the R session. It should be one of "skipgram", "cbow", "supervised", "test", "test-label" or "quantize" |
Value
It does not return a value but only prints the available parameters in the R session
References
https://github.com/facebookresearch/fastText#full-documentation
https://github.com/facebookresearch/fastText/issues/341#issuecomment-339783130
Examples
## Not run:
library(fastText)
print_parameters(command = 'supervised')
## End(Not run)