POS tagger Question: Should I keep embedding weights 0 if it is excluded from the Word2Vec training model? or should I set min_count to 1 for my training model?

I'm training a POS tagger (for multiple languages that may not have word-vector dataset) and intended to include a embedding layer with pre-trained weights produced by a self-trained model via Word2Vec using a training set.

I assume that the rows of the array for embedding weights need to resembles the number of unique words in the vectorised token dictionary (i.e., I have 15000 unique words + padding term in the 'term to index' dictionary --> 15001 rows for the embedding …

embedding languagetechnology training word2vec