when training word embedding models, high-frequency words can be subsampled
It uniformly samples an integer between 1 and max_window_size at random as the context window size. For any center word, those words whose distance from it does not exceed the sampled context window size are its context words.
In a minibatch, the � th example includes a center word and its � � context words and � � noise words. Due to varying context window sizes, � � + � � varies for different � . Thus, for each example we concatenate its context words and noise words in the contexts_negatives variable, and pad zeros until the concatenation length reaches max ...
To distinguish between positive and negative examples, we separate context words from noise words in contexts_negatives via a labels variable. Similar to masks, there is also a one-to-one correspondence between elements in labels and elements in contexts_negatives, where ones (otherwise zeros) in labels correspond to context words (positive example...
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