Peer review plays an essential role in validating academic work and elevating the quality of published research. Developing an accurate system for predicting peer-review scores can be challenging due to the restricted annotated peer-review information. This paper presents intermediate-task training to improve the efficiency of the pretrained models. Recent research has demonstrated the efficacy of pretrained language models for downstream tasks. Applying intermediate-task training can help a model learn beneficial information before fine-tuning it on a peer-review score prediction. We also extend a pretrained model to overcome one of its primary drawbacks - application to documents that are longer than a thousand words (e.g., academic papers). Our technique is conceptually straightforward. We partition a document into sentences and feed each one into a pretrained model to acquire sentence embedding. Then, we utilize a sequence of sentence embeddings as an input of that pretrained model. The experimental results demonstrated that intermediate-task training helps increase the performance of the pretrained model on peer-review score prediction.