Transformers have become a cornerstone in the field of natural language processing (NLP). This tutorial provides an overview of transformers, their architecture, and how they are used in various NLP tasks.

What are Transformers?

Transformers are a type of deep neural network architecture that is based on self-attention mechanisms. They are designed to process sequences of data, such as text, and have been shown to achieve state-of-the-art results in various NLP tasks.

Architecture

The architecture of a transformer consists of several key components:

  • Input Embeddings: The input text is converted into a sequence of embeddings.
  • Positional Encoding: To maintain the order of the words in the sequence.
  • Encoder: The encoder consists of multiple layers of self-attention and feed-forward neural networks.
  • Decoder: Similar to the encoder, but also includes a mechanism to attend to the output of the encoder.
  • Output Embeddings: The output of the decoder is converted back into the original text format.

Applications

Transformers have been successfully applied to a wide range of NLP tasks, including:

  • Text Classification: Classifying text into predefined categories.
  • Machine Translation: Translating text from one language to another.
  • Question Answering: Answering questions based on a given context.
  • Summarization: Generating summaries of long texts.

Further Reading

For more in-depth information on transformers, we recommend the following resources:

Transformers Architecture