📌 Transformer Decoders Summary
Transformer decoders are a component of the transformer neural network architecture, designed to generate sequences one step at a time. They work by taking in previously generated data and context information to predict the next item in a sequence, such as the next word in a sentence. Transformer decoders are often used in tasks that require generating text, like language translation or text summarisation.
🙋🏻♂️ Explain Transformer Decoders Simply
Imagine you are writing a story with a friend, and each of you takes turns adding one sentence at a time, using what has already been said to decide what comes next. A transformer decoder works in a similar way, using the words it has already generated to predict the next word, ensuring the story makes sense.
📅 How Can it be used?
Transformer decoders can be used to build a chatbot that generates human-like responses in customer service applications.
🗺️ Real World Examples
A transformer decoder is used in automatic email completion tools, where it predicts and suggests the next words or sentences as a user types, making it faster to compose emails.
Transformer decoders are used in machine translation systems, such as translating English text into French, by generating the translated sentence word by word based on the input and previous output.
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