What is the transformer model architecture?
The transformer model is a deep-learning architecture that has revolutionized natural language processing (NLP) and sequence-oriented tasks with the help of a self-attention mechanism. Introduced by Google’s researchers in the paper "
We'll discuss the technical details of the transformer model architecture by analyzing how it uses the self-attention mechanism and other techniques to process each input and maintain its sequential order using positional encoding.
Self-attention mechanism
The self-attention mechanism allows the transformer model to determine the importance of different positions in the input sequence for making predictions. Self-attention helps the model identify relevant parts of the sequence by comparing positions using query, key, and value matrices. By assigning weights to each position, self-attention calculates a weighted sum of the values and creates a context vector representing the sequence's most important parts. Self-attention enables transformer models to consider context and generate more accurate and nuanced outputs than traditional neural networks.
The transformer model architecture is depicted in the following illustration:
Let's explore each component of the transformer model architecture above.
Embeddings
Embeddings convert input or output tokens to numerical representations that the transformer model can process. This is a way to represent words as dense vectors of real numbers. This captures the contextual meanings of words and their relationships with other words in the sentence. The transformer model uses positional embeddings to find the order of each word in the input or output sequences.
Positional encoding
Positional encoding is utilized in the transformer model to incorporate information about the word order. The model itself lacks this built-in understanding. It involves adding a fixed vector representation to each word's embedding. By adding positional encoding to the embeddings, the model can distinguish between words based on their position within the sequence.
Multi-head attention
The multi-head attention allows the model to simultaneously focus on the distinct parts of the input sequence by splitting it into subsets to produce more robust results. It involves calculating weighted sums using queries, keys, and values and employing softmax functions. The multi-head attention mechanism enables the model to capture complex relationships between different parts of the input sequence, improving accuracy and effectiveness.
Feed-forward network
The position-wise feed-forward network improves the ability of the transformer model to predict answers by processing each position separately and in parallel to capture the dependencies between different positions effectively. It uses the rectified linear activation function, which multiplies the input vector with the weight matrix to calculate the output for each position in the input sequence. The feed-forward network has two advantages: it allows the model to learn nonlinear relationships between positions and reduces computational complexity due to parallel processing. The following illustration gives an overview of the feed-forward network:
Softmax function
The softmax function is applied to the text generated from the self-attention layer to normalize scores and assign probabilities to each output token. The output token with the highest probability is then selected to predict the answer. The transformer model uses
You can find more details on the softmax function in this Educative Answer.
Conclusion
The transformer model is a powerful architecture for predicting answers in natural language processing. It efficiently processes text, capturing word relationships through attention mechanisms. Its multi-head attention allows the simultaneous processing of different parts of the input. Despite being computationally expensive, recent innovations like GPT-3 have shown scalability and state-of-the-art performance, making the transformer a significant advancement in natural language processing.
