This paper contributed to the understudied task of literary MD by applying state-of-the-art NLP methodology, like fine-tuning the Transformer model DistilBERT and few-shot learning with the Sentence Transformer approach, on four literary metaphor datasets. Metaphor was defined quite narrowly as consisting of one of two specific POS sequences. The evaluation baseline was the combined rule-based and statistical approach of Kesarwani et al. (2017).