  Transformer-based models for transfer learning have the potential to achieve
high prediction accuracies on text-based supervised learning tasks with
relatively few training data instances. These models are thus likely to benefit
social scientists that seek to have as accurate as possible text-based measures
but only have limited resources for annotating training data. To enable social
scientists to leverage these potential benefits for their research, this paper
explains how these methods work, why they might be advantageous, and what their
limitations are. Additionally, three Transformer-based models for transfer
learning, BERT (Devlin et al. 2019), RoBERTa (Liu et al. 2019), and the
Longformer (Beltagy et al. 2020), are compared to conventional machine learning
algorithms on three applications. Across all evaluated tasks, textual styles,
and training data set sizes, the conventional models are consistently
outperformed by transfer learning with Transformers, thereby demonstrating the
benefits these models can bring to text-based social science research.
