Causal Language Modeling with GPT

The article provides a detailed guide on training a Causal Language Model using GPT-2 with the Hugging Face Transformers library, and tracking the process with Comet. It explains the difference between Causal Language Modeling and Masked Language Modeling, highlighting that the former is unidirectional and uses only the preceding text to predict the next token. The guide demonstrates the preparation of a text dataset, specifically the Wikitext, by tokenizing and grouping the data for efficient training. It outlines the setup of a Comet experiment to track various metrics and parameters during training, including the use of TensorFlow to convert the dataset into the proper format. The training process involves fine-tuning a pre-trained GPT-2 model at a low learning rate to prevent overfitting, followed by evaluation using perplexity as a metric. The article also covers generating text with the trained model and suggests experimenting with different language models for further exploration.

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