I’m looking at the http://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html 368
It says in the exercise section: “The Continuous Bag-of-Words model (CBOW) is frequently used in NLP deep learning. It is a model that tries to predict words given the context of a few words before and a few words after the target word. This is distinct from language modeling, since CBOW is not sequential and does not have to be probabilistic.”
What makes n-gram “sequential” or “probabilistic”? The only change to the n-gram code I did for this exercise was changing the trigram to “fivegram”, where the context is now 2+2 words before and after, rather than 2 words before the target word.
Am I misunderstanding something?