Transformer is a deep learning model that is widely used in natural language processing tasks such as text classification, sentiment analysis, machine translation, etc.
The following is an example of explaining Transformer using real life examples:
Suppose you are at a party and there are many people chatting. You want to know what the theme of the party is, but you don't want to ask the party organizer directly. At this time, Transformer can help you.
Transformer can analyze people's conversations at a party, find keywords and themes, and turn that information into a hashtag. For example, if people are talking about movies, music, and food, Transformer might label the party's topic as "Entertainment."
How does Transformer do this? It uses a technology called "attention mechanism", which is similar to human attention. When people are chatting, they will notice some keywords and topics and use this information to infer the theme of the party. Transformer is similar, it can infer the meaning of text by analyzing keywords and topics in text data.
In summary, Transformer is a powerful natural language processing model that can help us understand and process text data. It works similar to the human attention mechanism and can help us find useful information from large amounts of text data.