The Transformer is a type of deep learning model that completely changed how we handle tasks like language translation, summarization, and more. It was introduced in 2017 in a paper called “Attention is All You Need.
What makes the Transformer different is that, unlike older models that read text one word at a time (like RNNs), it looks at the whole sentence at once. It uses something called self-attention to figure out which words are most important in a sentence, no matter where they appear. This helps it understand the meaning and context more effectively
So instead of just moving from left to right through a sentence, the Transformer can focus on the right words at the right time — making it faster, more accurate, and perfect for tasks involving text or any type of sequence data.
At its core, a Transformer model consists of Encoder: Processes input sequences
Decoder: Generates outputs, often autoregressively
Attention Mechanism: Allows the model to focus on different parts of the input
This structure enables parallel computation, faster training, and superior performance on a range of tasks.
Variants of Transformer Architectures
Encoder-Only Transformers: Encoder-only Transformers, work by reading the entire input text at once and learning to deeply understand it. One of the keyways they do this is through a technique called Masked Language Modeling. During training, some words in the sentence are randomly replaced with a special token like [MASK]. The model’s job is to figure out what the missing word should be based on the context of the other words around it.
Examples of Encoder only Transformer models:
Use Cases: There are Specific Tasks Encoder Only Transformers excel at: Text classification Named Entity Recognition (NER) Sentence similarity Embedding generation
Decoder-Only Transformers: Decoder-only Transformers, are designed for one main job: generating text. Instead of trying to understand a full sentence all at once like encoder-only models do, decoder-only models work in an autoregressive way. That means they generate text one word at a time, each time predicting the next word based on the ones that came before it. To make this work, the model is trained by masking out future words during training. So while it sees the beginning of a sentence, it doesn’t get to “cheat” by looking ahead. Examples of Decoder only Transformer models
Use Cases: There are Specific Tasks Decoder Only Transformers excel at: Text completion Chatbots Code generation Story writing
Encoder-Decoder Transformers (Sequence-to-Sequence) : Encoder-decoder Transformers, also known as sequence-to-sequence models, combine both an encoder and a decoder to transform one sequence into another. The encoder first reads and understands the entire input — like a sentence in English — by creating a detailed internal representation of its meaning. Then, the decoder takes this understanding and generates a new sequence — such as the translated sentence in another language — one word at a time. The decoder predicts each word based on the encoded input and the words it has already generated, allowing the model to produce accurate and context-aware outputs.
Use Cases: There Specific Tasks Encoder-Decoder Transformers excel at : Machine translation Text summarization Question answering Speech-to-text
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