Semantic search is a very effective way to search documents with a query. In this article, you’ll learn how to use embeddings and similarity in order to build a semantic search model.

A huge roadblock for language models is when a word can be used in two different contexts. When this problem is encountered, the model needs to use the context of the sentence in order to decipher which meaning of the word to use. This is precisely what self-attention models do.

For large language models, it is crucial to know when two words, or two sentences, are similar or different. This can be a hard problem, but luckily, word and sentence embeddings are very helpful for this task. In this post we go over some different notions of similarity.

Sentence and word embeddings are the bread and butter of language models. Here is a very simple introduction to what they are.