The Transformer begins with constructing a dataset, extracting words, and building a vocabulary. Each word is assigned a unique index, simplifying the tokenization process.
Words are mapped into dense numerical representations. These embeddings capture syntactic and semantic properties of words and provide an effective input for the model.
Since Transformers do not have recurrence, positional encodings are added to input embeddings to retain word order information.
Multi-head attention enables the model to focus on different parts of the sequence simultaneously, learning contextual relationships between words.
Skip connections (residual connections) help gradient flow, reducing vanishing gradient problems, while layer normalization stabilizes learning.
Each position in the sequence independently undergoes two dense layers with a non-linear activation function, enhancing the model's expressiveness.
Layer normalization is applied again to prevent unstable updates and optimize training.
Similar to the encoder, the decoder processes input embeddings with positional encoding.
The decoder employs masked attention, ensuring that predictions for each word only depend on previously generated words.
The final linear layer maps the decoder output to a probability distribution over the vocabulary using softmax.
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