Package: fastTextR 2.1.0.9000

fastTextR: An Interface to the 'fastText' Library

An interface to the 'fastText' library <https://github.com/facebookresearch/fastText>. The package can be used for text classification and to learn word vectors. An example how to use 'fastTextR' can be found in the 'README' file.

Authors:Florian Schwendinger [aut], Emil Hvitfeldt [aut, cre]

fastTextR_2.1.0.9000.tar.gz
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fastTextR_2.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
fastTextR/json (API)
NEWS

# Install 'fastTextR' in R:
install.packages('fastTextR', repos = c('https://emilhvitfeldt.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/emilhvitfeldt/fasttextr/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

5.35 score 5 stars 1 packages 50 scripts 324 downloads 13 exports 2 dependencies

Last updated from:277d1aa231. Checks:6 WARNING, 7 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING148
linux-devel-x86_64WARNING150
source / vignettesOK226
linux-release-arm64WARNING143
linux-release-x86_64WARNING137
macos-release-arm64OK111
macos-release-x86_64OK239
macos-oldrel-arm64OK123
macos-oldrel-x86_64OK169
windows-develWARNING153
windows-releaseWARNING268
windows-oldrelOK142
wasm-releaseOK137

Exports:fasttextft_analogiesft_controlft_loadft_nearest_neighborsft_normalizeft_predictft_saveft_sentence_vectorsft_testft_trainft_word_vectorsft_words

Dependencies:Rcppslam

fastTextR - Cheatsheet

Rendered fromCheatsheet.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-12-05
Started: 2022-10-30

fastTextR - Text Classification

Rendered fromText_classification.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2022-10-30
Started: 2022-10-30

fastTextR - Word representations

Rendered fromWord_representations.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-12-05
Started: 2022-10-30

Readme and manuals

Help Manual

Help pageTopics
Create a New 'FastText' Modelfasttext
Get Analogiesft_analogies
Default Control Settingsft_control
Load Modelft_load
Get Nearest Neighborsft_nearest_neighbors
Normalizeft_normalize
Write Modelft_save
Get Sentence Vectorsft_sentence_vectors
Evaluate the Modelft_test
Train a Modelft_train
Get Word Vectorsft_word_vectors
Get Wordsft_words
Predict using a Previously Trained Modelft_predict predict.supervised_model