Transformer

Transformers are a type of neural network architecture designed to handle sequential data, primarily used in natural language processing tasks.

Transformers are widely used in tasks such as translation, text generation, and summarization. They are effective in capturing long-range dependencies in sequences, making them suitable for complex language tasks.

Modern transformers, such as BERT and GPT, are often pretrained on large datasets using self-supervised learning techniques and later fine-tuned on specific tasks.

Transformers use an encoder-decoder structure, where the encoder processes the input sequence and the decoder generates the output sequence. The key innovation is the self-attention mechanism, which allows the model to weigh the importance of different words in the input sequence, enabling it to focus on relevant parts of the data. Unlike standard encoder-decoder models that rely on recurrent neural networks (RNNs) or convolutional neural networks (CNNs), transformers do not process data sequentially. Instead, they use self-attention to process all words in the input simultaneously, which significantly improves parallelization and reduces training time. This approach also helps in capturing long-range dependencies more effectively than RNNs, which can struggle with vanishing gradient problems. However, since transformers do not process data sequentially like RNNs, they lack an inherent sense of order in the input sequence. To address this, positional encoding is added to provide information about word order.

For example, in a translation task, a transformer can translate a sentence from English to French by understanding the context and relationships between words in the input sentence and generate the output sentence accordingly.

In summary, transformers are powerful tools for natural language processing, leveraging self-attention to handle complex language patterns and dependencies. Their ability to process sequential data efficiently makes them essential for tasks like translation, text generation, and summarization.