Generates skipgram word pairs.

  window_size = 4,
  negative_samples = 1,
  shuffle = TRUE,
  categorical = FALSE,
  sampling_table = NULL,
  seed = NULL



A word sequence (sentence), encoded as a list of word indices (integers). If using a sampling_table, word indices are expected to match the rank of the words in a reference dataset (e.g. 10 would encode the 10-th most frequently occuring token). Note that index 0 is expected to be a non-word and will be skipped.


Int, maximum possible word index + 1


Int, size of sampling windows (technically half-window). The window of a word w_i will be [i-window_size, i+window_size+1]


float >= 0. 0 for no negative (i.e. random) samples. 1 for same number as positive samples.


whether to shuffle the word couples before returning them.


bool. if FALSE, labels will be integers (eg. [0, 1, 1 .. ]), if TRUE labels will be categorical eg. [[1,0],[0,1],[0,1] .. ]


1D array of size vocabulary_size where the entry i encodes the probabibily to sample a word of rank i.


Random seed


List of couples, labels where:

  • couples is a list of 2-element integer vectors: [word_index, other_word_index].

  • labels is an integer vector of 0 and 1, where 1 indicates that other_word_index was found in the same window as word_index, and 0 indicates that other_word_index was random.

  • if categorical is set to TRUE, the labels are categorical, ie. 1 becomes [0,1], and 0 becomes [1, 0].


This function transforms a list of word indexes (lists of integers) into lists of words of the form:

  • (word, word in the same window), with label 1 (positive samples).

  • (word, random word from the vocabulary), with label 0 (negative samples).

Read more about Skipgram in this gnomic paper by Mikolov et al.: Efficient Estimation of Word Representations in Vector Space

See also