tf.keras.preprocessing.sequence.skipgrams( sequence, vocabulary_size, window_size=4, negative_samples=1.0, shuffle=True, categorical=False, sampling_table=None, seed=None )
Generates skipgram word pairs.
This function transforms a sequence of word indexes (list of integers) into tuples 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
sequence: 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 occurring token). Note that index 0 is expected to be a non-word and will be skipped.
vocabulary_size: Int, maximum possible word index + 1
window_size: Int, size of sampling windows (technically half-window). The window of a word
[i - window_size, i + window_size+1].
negative_samples: Float >= 0. 0 for no negative (i.e. random) samples. 1 for same number as positive samples.
shuffle: Whether to shuffle the word couples before returning them.
categorical: bool. if False, labels will be integers (eg.
[0, 1, 1 .. ]), if
True, labels will be categorical, e.g.
[[1,0],[0,1],[0,1] .. ].
sampling_table: 1D array of size
vocabulary_sizewhere the entry i encodes the probability to sample a word of rank i.
seed: Random seed.
couples, labels: where `couples` are int pairs and `labels` are either 0 or 1.
By convention, index 0 in the vocabulary is a non-word and will be skipped.