This is a TensorFlow implementation of a Transformer-based language model. The Transformer architecture is a powerful model for various natural language processing tasks, including language generation and translation. This particular implementation focuses on language generation tasks.
The Transformer model consists of encoder and decoder blocks, each containing multi-head self-attention layers and feed-forward neural networks. The model learns to generate text autoregressively, predicting the next token based on the preceding context.
Install TensorFlow and its dependencies. Make sure to have an input.txt file containing the text corpus for training. Update the configuration parameters such as block_size, batch_size, learning_rate, etc., according to your requirements. Run the script, which will train the Transformer model on the provided text corpus. After training, you can test the model's generation capability by calling the generate() function and passing an initial sequence of tokens.
TensorFlow Numpy
This implementation is based on the Transformer architecture proposed in the paper "Attention is All You Need" by Vaswani et al.