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TensorFlow 1 version
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View source on GitHub
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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
)
Used in the notebooks
| Used in the tutorials |
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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 | |
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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.
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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].
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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
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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] .. ].
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sampling_table
|
1D array of size vocabulary_size where the entry i
encodes the probability to sample a word of rank i.
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seed
|
Random seed. |
Returns | |
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couples, labels: where couples are int pairs and
labels are either 0 or 1.
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Note:
By convention, index 0 in the vocabulary is a non-word and will be skipped.