Efficient Learning of Word Representations and Sentence Classification
elapsed time in hours & minutes & seconds
Interface for the fasttext library
The Rcpp function which is used in the 'fasttext_interface' R function
inner function of 'compute_elapsed_time'
Language Identification using fastText
Multiple plot function
Plot the progress of loss, learning-rate and word-counts
Print the parameters for a specific command
Print Usage Information when the command equals to 'analogies'
Print Usage Information when the command equals to 'dump'
Print Usage Information when the command equals to 'nn'
Print Usage Information when the command equals to 'predict' or 'predi...
Print Usage Information when the command equals to 'print-ngrams'
Print Usage Information when the command equals to 'print-sentence-vec...
Print Usage Information when the command equals to 'print-word-vectors...
Print Usage Information when the command equals to 'quantize'
Print Usage Information when the command equals to 'test-label'
Print Usage Information when the command equals to 'test'
Print Usage Information for all parameters
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>.
Useful links