Definition: Transformer models are a type of neural network architecture that are particularly effective for processing sequential data, such as text and time series.

Detailed Description:

Transformer models use a mechanism called attention to weigh the importance of different parts of the input sequence when making predictions. This allows them to capture long-range dependencies in data, making them suitable for tasks like machine translation, text summarization, and question answering.

Transformer models have achieved state-of-the-art results on many natural language processing benchmarks.

Related Terms:

• Neural Networks
• Natural Language Processing
• Attention Mechanism
• Sequence-to-Sequence Modeling
• Encoder-Decoder Architecture

FAQs:

1. What is the difference between RNNs and transformer models?

   • RNNs use recurrent connections to process sequential data, while transformer models use attention mechanisms. Transformer models can capture long-range dependencies more effectively than RNNs.

2. What is the attention mechanism in transformer models?

   • The attention mechanism allows the model to weigh the importance of different parts of the input sequence when making predictions.

3. What are some applications of transformer models?

   • Machine translation, text summarization, question answering, and text generation.