Comments (1)
bentrevett
commented on May 23, 2024
All words not in the GloVe vocabulary will have their embedding initialized to a normally distributed vector, which is what the unk_init does. As the GloVe embedding only contains single words, then all bi-grams will be initialized in this way.
"love u" will be set to UNK only if it is not in the 25,000 most common tokens/bi-grams. If you have a lot of examples then "love u" will be in the vocabulary and thus not UNK'd. This will depend on your dataset, but the main reason for using bi-grams is that there in fact will be plenty of bi-grams that appear enough times to appear in the 25,000 most common tokens/bi-grams and won't be UNK'd. See this comment: #69, which shows that in the IMDB dataset 65% of the vocabulary is actually bi-grams.
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Related Issues (20)
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