tf.keras.preprocessing.sequence.make_sampling_table      View source on GitHub    Generates a word rank-based probabilistic sampling table.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.preprocessing.sequence.make_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.  
  

    
{'path': 'public/docs/tensorflow~2.9/keras/preprocessing/sequence/make_sampling_table.html', 'source': 'DevDocs.IO'}