This provides an overview and guidance for implementing the "Attention is All You Need" paper by Vaswani et al. (2017). The paper introduces the Transformer architecture, a model based solely on self-attention mechanisms, achieving state-of-the-art results in various natural language processing tasks.
The Transformer model introduced in the paper "Attention is All You Need" is a revolutionary neural network architecture that relies on the self-attention mechanism. This implementation aims to provide a clear and concise codebase for understanding and applying the Transformer model.
Install the required dependencies using:
pip install -r requirements.txt
The model architecture is based on the Transformer described in the paper. The key components include:
- Encoder: Composed of multiple layers, each containing a multi-head self-attention mechanism and a feedforward neural network.
- Decoder: Similar to the encoder but also includes an additional multi-head attention layer to attend to the encoder's output.
For a detailed explanation of the architecture, refer to Section 3 of the original paper.
To train the Transformer model, run the following command:
python trainer.py
This is a sample for a translator from English to Italian with this Transformer model: