  Despite the popularity of the `pre-train then fine-tune' paradigm in the NLP
community, existing work quantifying energy costs and associated carbon
emissions has largely focused on language model pre-training. Although a single
pre-training run draws substantially more energy than fine-tuning, fine-tuning
is performed more frequently by many more individual actors, and thus must be
accounted for when considering the energy and carbon footprint of NLP. In order
to better characterize the role of fine-tuning in the landscape of energy and
carbon emissions in NLP, we perform a careful empirical study of the
computational costs of fine-tuning across tasks, datasets, hardware
infrastructure and measurement modalities. Our experimental results allow us to
place fine-tuning energy and carbon costs into perspective with respect to
pre-training and inference, and outline recommendations to NLP researchers and
practitioners who wish to improve their fine-tuning energy efficiency.
