Train a GPT to write like Shakespeare

The provided markdown data is a Python script that uses the PyTorch library to build a causal transformer model for text generation. The script begins by importing necessary libraries and downloading a dataset of 40,000 lines of Shakespeare's plays. It then defines a Tokenizer class to convert characters to tokens and vice versa. The CausalTransformer class is defined next, which uses the PyTorch TransformerDecoder for self-attention and includes methods for forward propagation.

The main function of the script reads the text data, tokenizes it, and initializes the CausalTransformer model. It then enters a training loop where it fetches batches of data, computes the cross-entropy loss, and updates the model parameters. Every few iterations, it generates new text using the current state of the model. The script ends by generating a final piece of text after training is complete. The script can be run from the command line with customizable parameters using the Fire library.

Key takeaways:

The script is written in Python and uses the PyTorch library to build a CausalTransformer model for text generation.
The model is trained on a dataset of 40,000 lines of Shakespeare's plays, which is downloaded from a URL using the subprocess module.
The script includes a Tokenizer class to encode and decode the text into tokens for the model to process.
The script also includes a main function that trains the model, generates text at regular intervals during training, and finally generates a longer piece of text after training is complete.

Train a GPT to write like Shakespeare

Key takeaways:

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