A bag-of-$n$-grams model is a way to represent a document, similar to a [bag-of-words][/terms/bag-of-words/] model.

A bag-of-$n$-grams model represents a text document as an unordered collection of its $n$-grams.

For example, let’s use the following phrase and divide it into bi-grams ($n=2$).

James is the best person ever.

becomes

• <start>James
• James is
• is the
• the best
• best person
• person ever.
• ever.<end>

In a typical bag-of-$n$-grams model, these 6 bigrams would be a sample from a large number of bigrams observed in a corpus. And then James is the best person ever. would be encoded in a representation showing which of the corpus’s bigrams were observed in the sentence.

A bag-of-$n$-grams model has the simplicity of the bag-of-words model, but allows the preservation of more word locality information.