The key components of a transformer architecture are as follows:
Encoder: The encoder processes the input sequence, such as a sentence or a document, and transforms it into a set of representations that capture the contextual information of each input element. The encoder consists of multiple identical layers, each containing a self-attention mechanism and position-wise feed-forward neural networks. The self-attention mechanism allows the model to attend to different parts of the input sequence while encoding it.
Decoder: The decoder takes the encoded representations generated by the encoder and generates an output sequence. It also consists of multiple identical layers, each containing a self-attention mechanism and additional cross-attention mechanisms. The cross-attention mechanisms enable the decoder to attend to relevant parts of the encoded input sequence when generating the output.
Self-Attention: Self-attention is a mechanism that allows the transformer to weigh the importance of different elements in the input sequence when generating representations. It computes attention scores between each element and every other element in the sequence, resulting in a weighted sum of the values. This process allows the model to capture dependencies and relationships between different elements in the sequence.
Positional Encoding: Transformers incorporate positional encoding to provide information about the order or position of elements in the input sequence. This encoding is added to the input embeddings and allows the model to understand the sequential nature of the data.
Feed-Forward Networks: Transformers utilize feed-forward neural networks to process the representations generated by the attention mechanisms. These networks consist of multiple layers of fully connected neural networks with activation functions, enabling non-linear transformations of the input representations.
Screenshot-from-2019-06-17-19-53-10
The transformer architecture is widely used in NLP tasks due to several reasons:
Self-Attention Mechanism: Transformers leverage a self-attention mechanism that allows the model to focus on different parts of the input sequence during processing. This mechanism enables the model to capture long-range dependencies and contextual information efficiently, making it particularly effective for tasks that involve understanding and generating natural language.
Parallelization: Transformers can process the elements of a sequence in parallel, as opposed to recurrent neural networks (RNNs) that require sequential processing. This parallelization greatly accelerates training and inference, making transformers more computationally efficient.
Scalability: Transformers scale well with the length of input sequences, thanks to the self-attention mechanism. Unlike RNNs, transformers do not suffer from the vanishing or exploding gradient problem, which can hinder the modeling of long sequences. This scalability makes transformers suitable for tasks that involve long texts or documents.
Transfer Learning: Transformers have shown great success in pre-training and transfer learning. Models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are pre-trained on massive amounts of text data, enabling them to learn rich representations of language. These pre-trained models can then be fine-tuned on specific downstream tasks with comparatively smaller datasets, leading to better generalization and improved performance.
Contextual Understanding: Transformers excel in capturing the contextual meaning of words and sentences. By considering the entire input sequence simultaneously, transformers can generate more accurate representations that incorporate global context, allowing for better language understanding and generation.
