tf.keras.preprocessing.sequence.make_sampling_table | TensorFlow v2.16.1 (original) (raw)
tf.keras.preprocessing.sequence.make_sampling_table
Generates a word rank-based probabilistic sampling table.
tf.keras.preprocessing.sequence.make_sampling_table(
size, sampling_factor=1e-05
)
Used in the notebooks
| Used in the tutorials |
|---|
| word2vec |
DEPRECATED.
Used for generating the sampling_table argument for skipgrams.sampling_table[i] is the probability of sampling the word i-th most common word in a dataset (more common words should be sampled less frequently, for balance).
The sampling probabilities are generated according to the sampling distribution used in word2vec:
p(word) = (min(1, sqrt(word_frequency / sampling_factor) /
(word_frequency / sampling_factor)))
We assume that the word frequencies follow Zipf's law (s=1) to derive a numerical approximation of frequency(rank):
frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank))where gamma is the Euler-Mascheroni constant.
| Args | |
|---|---|
| size | Int, number of possible words to sample. |
| sampling_factor | The sampling factor in the word2vec formula. |
| Returns |
|---|
| A 1D Numpy array of length size where the ith entry is the probability that a word of rank i should be sampled. |
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Last updated 2024-06-07 UTC.