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
DESCRIPTION |NEWS
card.svg |card.png
fastTextR/json (API)

# 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

5.40 score 5 stars 1 packages 56 scripts 393 downloads 13 exports 2 dependencies

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

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macos-release-arm64OK106
macos-release-x86_64OK420
macos-oldrel-arm64OK87
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Exports:fasttextft_analogiesft_controlft_loadft_nearest_neighborsft_normalizeft_predictft_saveft_sentence_vectorsft_testft_trainft_word_vectorsft_words

Dependencies:Rcppslam

fastTextR - Cheatsheet

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

fastTextR - Word representations
Load pretrained model | Printing word vectors | Printing sentence vectors | Nearest neighbor queries | Word analogies | References

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

fastTextR - Text Classification
Download Data | Normalize Data | Train Model | Read Model | Predict / Test Model | References

Last update: 2022-10-30
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