Day 33: Building a Mini Transformer From Scratch (Code Walkthrough)
Transformers are the architecture that reshaped natural language processing (NLP) in 2017. Before transformers, top-performing models used either recurrent neural networks (RNNs) or convolutional neural networks (CNNs). Transformers delivered dramatic improvements in tasks like machine translation, text summarization, and question answering. Their main strength is the attention mechanism , which…
Transformers revolutionized natural language processing in 2017, replacing older architectures like RNNs and CNNs. Their standout feature, self-attention, enables models to focus on all words in a sentence, not just nearby ones. This allows for richer context-aware representations and efficient parallel computation. The core of self-attention involves creating three vectors for each input token: Query, Key, and Value.
These vectors are derived from the token embedding by multiplying it with small matrices. The model then calculates a score for each pair of tokens by taking the dot product of the Query and Key vectors. This score is scaled and normalized using the softmax function to produce attention weights, indicating how much to focus on each token.
The final step involves computing a weighted sum of the Value vectors using these attention weights, resulting in a new embedding for each token. This process is repeated across multiple layers, with each layer consisting of a Multi-Head Self-Attention block and a Feedforward block. The MiniTransformer class demonstrates these components using NumPy, creating random token embeddings and projection matrices for Query, Key, and Value vectors.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
