How does a transformer work?
1 Answer
A Transformer is a deep learning model architecture that was introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. It has since become the foundation for various natural language processing (NLP) tasks due to its outstanding performance and efficiency. The Transformer architecture is primarily based on the attention mechanism, which allows the model to focus on relevant parts of the input when making predictions.
Here's a high-level overview of how a Transformer works:
Input Representation:
The input to a Transformer consists of sequences of tokens, which can represent words, subwords, or characters. Each token is first converted into a fixed-length vector called an embedding. These embeddings represent the token in a continuous vector space, capturing semantic relationships between tokens.
Positional Encoding:
Since Transformers don't have inherent positional information (unlike sequences in recurrent neural networks), positional encodings are added to the input embeddings. These encodings help the model understand the order of tokens in the sequence.
Encoder-Decoder Architecture (for sequence-to-sequence tasks):
The Transformer can be used for both encoder-only tasks, like language modeling, and encoder-decoder tasks, like machine translation. In the encoder-decoder architecture, the model is divided into an encoder and a decoder.
Encoder:
The encoder takes the input sequence, applies self-attention mechanisms, and processes it layer by layer. Each layer contains a multi-head self-attention mechanism followed by a feed-forward neural network. Self-attention allows the model to weigh the importance of different words in the input sequence while generating a representation for each word. The feed-forward network further refines these representations.
Self-Attention Mechanism:
The self-attention mechanism calculates attention scores between all pairs of words in the input sequence. These scores are used to weigh the importance of each word concerning the others, capturing dependencies and relationships within the sequence. The attention mechanism can be thought of as a query-key-value mechanism, where the "query" is used to search for relevant "keys" in the "values."
Multi-Head Attention:
Instead of using a single attention mechanism, Transformers employ multiple attention heads, allowing the model to capture different types of relationships in the data.
Decoder:
In the case of an encoder-decoder architecture, the decoder takes the output from the encoder and generates the final sequence. It also utilizes self-attention and encoder-decoder attention mechanisms. The self-attention helps the decoder focus on relevant parts of the output generated so far, while the encoder-decoder attention allows the decoder to attend to the relevant input information during the generation process.
Output Generation:
The decoder uses the final representations generated after several layers to predict the output sequence. In NLP tasks like language modeling or machine translation, a softmax layer is often applied to the output to obtain a probability distribution over the vocabulary, allowing the model to generate the most likely token at each step.
By leveraging self-attention and parallelization, Transformers can handle long sequences efficiently, making them a powerful choice for various sequence-related tasks in natural language processing and beyond.
Here's a high-level overview of how a Transformer works:
Input Representation:
The input to a Transformer consists of sequences of tokens, which can represent words, subwords, or characters. Each token is first converted into a fixed-length vector called an embedding. These embeddings represent the token in a continuous vector space, capturing semantic relationships between tokens.
Positional Encoding:
Since Transformers don't have inherent positional information (unlike sequences in recurrent neural networks), positional encodings are added to the input embeddings. These encodings help the model understand the order of tokens in the sequence.
Encoder-Decoder Architecture (for sequence-to-sequence tasks):
The Transformer can be used for both encoder-only tasks, like language modeling, and encoder-decoder tasks, like machine translation. In the encoder-decoder architecture, the model is divided into an encoder and a decoder.
Encoder:
The encoder takes the input sequence, applies self-attention mechanisms, and processes it layer by layer. Each layer contains a multi-head self-attention mechanism followed by a feed-forward neural network. Self-attention allows the model to weigh the importance of different words in the input sequence while generating a representation for each word. The feed-forward network further refines these representations.
Self-Attention Mechanism:
The self-attention mechanism calculates attention scores between all pairs of words in the input sequence. These scores are used to weigh the importance of each word concerning the others, capturing dependencies and relationships within the sequence. The attention mechanism can be thought of as a query-key-value mechanism, where the "query" is used to search for relevant "keys" in the "values."
Multi-Head Attention:
Instead of using a single attention mechanism, Transformers employ multiple attention heads, allowing the model to capture different types of relationships in the data.
Decoder:
In the case of an encoder-decoder architecture, the decoder takes the output from the encoder and generates the final sequence. It also utilizes self-attention and encoder-decoder attention mechanisms. The self-attention helps the decoder focus on relevant parts of the output generated so far, while the encoder-decoder attention allows the decoder to attend to the relevant input information during the generation process.
Output Generation:
The decoder uses the final representations generated after several layers to predict the output sequence. In NLP tasks like language modeling or machine translation, a softmax layer is often applied to the output to obtain a probability distribution over the vocabulary, allowing the model to generate the most likely token at each step.
By leveraging self-attention and parallelization, Transformers can handle long sequences efficiently, making them a powerful choice for various sequence-related tasks in natural language processing and beyond.