Machine learning heavily relies on the availability and quality of datasets, which are pivotal for training and evaluating machine learning models. Datasets play a critical role in natural language processing, ranging from tokenization processes to the understanding of scaling laws that govern the effectiveness of large language models. Researchers and practitioners must carefully navigate size, performance, and practical considerations to create efficient models, and contend with the challenges of copyright, licensing, data imbalances, and ethical issues when using different datasets.
