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What you are describing could be modeled as bipartite graph, one set of nodes connects to another set of nodes. Thus, it becomes a bipartite graph matching problem. As the size of each graph grows it quickly becomes intractable, then approximate nearest neighbor search might be useful.


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You don't need to make preprocessing as I understand, and the reason for this is that the Transformer makes an internal "dynamic" embedding of words that are not the same for every word; instead, the coordinates change depending on the sentence being tokenized due to the positional encoding it makes. Note the difference with Word2Vec, GloVe or ...


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For any kind of preprocessing/feature selection process, domain knowledge is the primary and most important tool that one can have. So even if your model or the test you have done on the data says you should remove certain data, you should still keep it. Having said that, in your case you try some of the following things, in the same order:- 1.) If a certain ...


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There is no perfect answer about dealing with NA values: Sometimes it makes sense to completely remove a feature which has a high proportion of NAs (from all the instances). But in your case there are only 3 features, so this would lead to a huge loss of information. Imputing the missing values with the mean of the feature is another option indeed, but ...


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