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Natural language processing (NLP) tasks (text classification, named entityrecognition, etc.) have seen revolutionary improvements over the last fewyears. This is due to language models such as BERT that achieve deep knowledgetransfer by using a large pre-trained model, then fine-tuning the model onspecific tasks. The BERT architecture has shown even better performance ondomain-specific tasks when the model is pre-trained using domain-relevanttexts. Inspired by these recent advancements, we have developed NukeLM, anuclear-domain language model pre-trained on 1.5 million abstracts from theU.S. Department of Energy Office of Scientific and Technical Information (OSTI)database. This NukeLM model is then fine-tuned for the classification ofresearch articles into either binary classes (related to the nuclear fuel cycle[NFC] or not) or multiple categories related to the subject of the article. Weshow that continued pre-training of a BERT-style architecture prior tofine-tuning yields greater performance on both article classification tasks.This information is critical for properly triaging manuscripts, a necessarytask for better understanding citation networks that publish in the nuclearspace, and for uncovering new areas of research in the nuclear (ornuclear-relevant) domains.

NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains