tf.keras.preprocessing.sequence.skipgrams

tf.keras.preprocessing.sequence.skipgrams(
    sequence,
    vocabulary_size,
    window_size=4,
    negative_samples=1.0,
    shuffle=True,
    categorical=False,
    sampling_table=None,
    seed=None
)

Defined in tensorflow/python/keras/_impl/keras/preprocessing/sequence.py.

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

Arguments:

• 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.