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There are two primary innovations that transformer models bring to the table. Consider these two innovations within the context of predicting text.

Positional encoding: Instead of looking at each word in the order that it appears in a sentence, a unique number is assigned to each word. This provides information about the position of each token (parts of the input such as words or subword pieces in NLP) in the sequence, allowing the model to consider the sequence's sequential information.

Self-attention: Attention is a mechanism that calculates weights for every word in a sentence as they relate to every other word in the sentence, so the model can predict words which are likely to be used in sequence. This understanding is learned over time as a model is trained on lots of data. The self-attention mechanism allows each word to attend to every other word in the sequence in parallel, weighing their importance for the current token. In this way, it can be said that machine learning models can “learn” the rules of grammar, based on statistical probabilities of how words are typically used in language.

How do transformer models work?
Transformer models work by processing input data, which can be sequences of tokens or other structured data, through a series of layers that contain self-attention mechanisms and feedforward neural networks. The core idea behind how transformer models work can be broken down into several key steps.

Let’s imagine that you need to convert an English sentence into French. These are the steps you’d need to take to accomplish this task with a transformer model.

Input embeddings: The input sentence is first transformed into numerical representations called embeddings. These capture the semantic meaning of the tokens in the input sequence. For sequences of words, these embeddings can be learned during training or obtained from pre-trained word embeddings.

Positional encoding: Positional encoding is typically introduced as a set of additional values or vectors that are added to the token embeddings before feeding them into the transformer model. These positional encodings have specific patterns that encode the position information.

Multi-head attention: Self-attention operates in multiple "attention heads" to capture different types of relationships between tokens. Softmax functions, a type of activation function, are used to calculate attention weights in the self-attention mechanism.

Layer normalization and residual connections: The model uses layer normalization and residual connections to stabilize and speed up training.

Feedforward neural networks: The output of the self-attention layer is passed through feedforward layers. These networks apply non-linear transformations to the token representations, allowing the model to capture complex patterns and relationships in the data.

Stacked layers: Transformers typically consist of multiple layers stacked on top of each other. Each layer processes the output of the previous layer, gradually refining the representations. Stacking multiple layers enables the model to capture hierarchical and abstract features in the data.

Output layer: In sequence-to-sequence tasks like neural machine translation, a separate decoder module can be added on top of the encoder to generate the output sequence.

Training: Transformer models are trained using supervised learning, where they learn to minimize a

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