Transformer

Transformers in Machine Learning

A transformer in machine learning (ML) refers to a deep learning model architecture designed to process sequential data, such as natural language processing (NLP). It was introduced in the paper “Attention Is All You Need” and has since become a cornerstone in NLP tasks.

Key Concepts of a Transformer

1. Architecture Overview:

   • A transformer model consists of an encoder and a decoder, although some models use only the encoder (like BERT) or only the decoder (like GPT). Each of these components is made up of layers that include mechanisms for attention and Feed Forward Neural Network.

2. Self-Attention Mechanism:

   • The core innovation of transformers is the self-attention mechanism, which allows the model to weigh the importance of different words in a sentence relative to each other. This is crucial for understanding context and relationships in language. See [Attention mechanism].
   • Scaled Dot-Product Attention: For each word in a sentence, the model computes attention scores with every other word. These scores are used to create a weighted representation of the input, emphasizing relevant words and de-emphasizing less relevant ones.

3. Multi-head attention

   • Instead of having a single attention mechanism, transformers use multiple attention heads. Each head learns different aspects of the relationships between words, allowing the model to capture various linguistic features.

4. Positional Encoding:

   • Since transformers do not inherently understand the order of words (unlike RNNs), they use positional encoding to inject information about the position of each word in the sequence.

5. Feed-Forward Neural Network:

   • After the attention mechanism, the output is passed through a feed-forward neural network, which is applied independently to each position.

6. Layer Normalization and Residual Connections:

   • Transformers use layer normalization and residual connections to stabilize training and help with gradient flow, making it easier to train deep networks.

7. Training and Applications:

   • Transformers are trained on large corpora of text data using unsupervised or semi-supervised learning techniques. They are used for a variety of NLP tasks, including translation, summarization, and question answering.

Additional Concepts

• Encoder-Decoder Structure:

  • The encoder processes the input sequence to build a representation, while the decoder takes this representation and generates the output sequence. This setup is particularly useful for tasks like translation.

• Parallelization:

  • Unlike Recurrent Neural Networks (RNNs), transformers do not require sequential processing, making them more efficient, especially when training large datasets.