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Pre-trained encoder-decoder transformer architectures have becomeincreasingly popular recently with the advent of T5 models. T5 has also becomemore favorable over other architectures like BERT due to the amount of datathat it is pre-trained on, increased scale of model parameter sizes and easyapplicability to a diverse set of tasks due to the generative nature of themodel. While being able to generalize to a wide variety of tasks, it is notclear that encoder-decoder architectures are the most efficient for fine-tuningtasks that don't require auto-regressive decoding. In this work, we studyfine-tuning pre-trained encoder-decoder models for tasks such asclassification, multi-label classification, and structured prediction. Wepropose \textbf{EncT5}, a framework for these problems, and illustrateinstantiations for these tasks. Our experiment results show that EncT5 hasadvantages over T5 such as efficiency and usability out performs BERT whenevaluated on publicly available pre-trained checkpoints.

EncT5: A Framework for Fine-tuning T5 as Non-autoregressive Models