|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/preprocessing/sequence.py#L248-L285) | Generates a word rank-based probabilistic sampling table. tf.keras.preprocessing.sequence.make_sampling_table( size, sampling_factor=1e-05 ) 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. ||