Transformer architecture is a deep learning model that has revolutionized the field of natural language processing (NLP). It is the foundation for models like BERT, GPT, and others, which have achieved state-of-the-art performance on various NLP tasks.
Key Components of Transformer
- Self-Attention Mechanism: This mechanism allows the model to weigh the importance of different words in the input sequence when generating the output.
- Feed-Forward Neural Networks: These networks are used to transform the input embeddings into the desired output embeddings.
- Positional Encoding: Since the Transformer model does not have an inherent understanding of the sequence order, positional encoding is added to the input embeddings to capture the order of the words.
Applications of Transformer
- Machine Translation: Transformer models have achieved impressive results in machine translation tasks.
- Text Summarization: Transformer models can be used to generate summaries of long texts.
- Question Answering: Transformer models can be fine-tuned for question answering tasks.
Further Reading
For a more detailed understanding of Transformer architecture, you can refer to the original paper:
Transformer Architecture Diagram