Since Transformer LLMs require fine-tuning on relatively large datasets which is a challenge for literary metaphor datasets, as can be seen from the sizes of the Graph Project’s datasets, this paper also employs the SetFit framework (Tunstall et al. 2022). It is designed for few-shot learning, that is, learning with a small number of labelled examples. SetFit operates in two steps: It generates sentence pairs, thereby enlarging the dataset significantly, and then fine-tunes embeddings of these sentences to create a sentence transformer embedding model. Then the dataset is used to train a logistic regression classifier using the fine-tuned embeddings to predict the right labels. The impact of the Sentence Transformer on the dataset size is immense: From 441 samples of the PoFo train dataset, SetFit generated 98366 unique pairs. The all-MiniLM-L6-v2 Sentence Transformer model is used within this framework because it is 5 times faster than larger models while maintaining comparable performance (Reimers et al. 2019). For implementation, the same test-train split ratio, seed and hyperparameters are used as for the Transformers implementation to maintain comparability.