I have a data that consists of DNA-sequences, where the words represented as kmers of length 6, and the sentences represented as DNA-sequences. Each DNA-sequence has 80 kmers (words)
The list of kmers I have is around 130,000 kmers, but after removing the duplicate elements, I would have 4500 kmers only. So, this huge gap confused me in regarding removing the duplicate kmers or not. My question is, is it recommended in this case to remove the duplicated kmers in the word2vec algorithm?
Thanks.
Without an example, it's unclear what you mean by "removing the duplicate elements". (Does that mean, when the same token appears twice in a row? Or twice in one "sentence"? Or, as I'm not familiar with what your data looks like in this domain, something else entirely?)
That you say there are 130,000 tokens in the vocabulary, but then 4,500 later, is also confusing. Typically the "vocabulary" size is the number of unique tokens. Removing duplicate tokens couldn't possibly change the number of unique tokens encountered.
In the usual domain of word2vec, natural language, words don't often repeat one-after-another. To the extent they sometimes might – as in say the utterance "it's very very hot in here" – it's not really an important enough case that I've noticed anyone commenting about handling that "very very" differently than any other two words.
(If a corpus had some artificially-duplicated full-sentences, it might be the case that you'd want to try discarding the exact-duplicate-sentences. Word2vec benefits from a variety of different usage-examples. Repeating the same sentence 10 times essentially just overweights those training examples – it's not nearly as good as 10 contrasting, but still valid, examples of the same words' usage.)
You're in a different domain that's not natural language, with different co-occurrence frequencies, and different end-goals. Word2vec might prove useful, but it's unlikely any general rules-of-thumb or recommendations from other domains will be useful. You should test things both ways, evaluate the results on your ultimate task in a robust repeatable way, and choose based on what you discover.