For retrosynthesis frameworks primarily targeting synthetic chemists, it is essential that the framework can provide a diverse selection of reactions that encompass a variety of underlying chemical transformations. Assessing this diversity necessitates the categorisation of each predicted reaction into distinct classes. These categories are drawn from the RXNO (Reaction Ontology) , which comprises ten distinct categories, including carbon-carbon bond formation, heteroatom alkylation and arylation, and protections. To perform this categorisation, a logistic regression classifier is employed. The classifier takes as input a latent reaction fingerprint, which is derived from the fine-tuning of the BERT model introduced by Schwaller et al. on the USPTO-50k dataset, as depicted in Figure . The classifier exhibits a remarkable level of confidence, achieving an accuracy of 99.5% on a separate test set from the USPTO-50k dataset. This high level of accuracy underscores its efficiency as a valuable tool for reaction classification. To quantitatively measure the diversity of reaction, a simple count is performed per molecular target (i.e. for all k reactions). Mathematically, the diversity is calculated as follows: