Transformer Details

Transformers are a cornerstone of natural language generation (NLG). Below are some key details about transformers.

• Core Components:

  • Self-Attention Mechanism: Allows the model to weigh the importance of different parts of the input text.
  • Encoder-Decoder Structure: Encoder processes the input sequence and decoder generates the output sequence.
  • Positional Encoding: Adds information about the position of words in the sequence.

• Applications:

  • Text Generation
  • Machine Translation
  • Summarization

• Advantages:

  • Efficient in handling long sequences.
  • Can capture complex dependencies in the text.

For more information on transformers, you can read about Transformer Models.

Transformer Architecture