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hi welcome to the video were going to explore how we can use sentence transformers and sentence embeddings in nlp for semantic similarity applications now in in the video were going to have a quick recap on transformers and where they came from so were going to have a quick look at recurring neural networks and the attention mechanism and then were going to move on to trying to define you know what is the difference between a transformer and a sentence transformer and also understanding okay why are these embeddings that are produced by transformers or sentence transformers specifically so good and at the end were also going to go through how we can implement our own sentence transformers in python as well so i think we should just jump straight into it [Applause] before we dive into sentence transformers i think it would make a lot of sense if we piece together where transformers come from with the intention of trying to understand why we use transformers now rather than some ot
