|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/preprocessing/sequence.py#L288-L385) | Generates skipgram word pairs. tf.keras.preprocessing.sequence.skipgrams( sequence, vocabulary_size, window_size=4, negative_samples=1.0, shuffle=True, categorical=False, sampling_table=None, seed=None ) 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](http://arxiv.org/pdf/1301.3781v3.pdf)


| ## Args || |---|---| | `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 `w_i` will be `[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_size` where the entry i encodes the probability to sample a word of rank i. | | `seed` | Random seed. |



| ## Returns || |---|---| | couples, labels: where `couples` are int pairs and `labels` are either 0 or 1. ||



| ## Note || |---|---| | By convention, index 0 in the vocabulary is a non-word and will be skipped. ||