This CMU Machine Learning Course is using the Bag-of-words model without too much explanation.
wiki uses the term multiplicity to explain that model.
The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity.
the link to explain multiplicity is in mathematical perspective, could someone please give a concrete example to explain multiplicity of Bag-of-words model in NLP perspective?