Allowing the model to fine-tune for longer than 12 hours improves the results further for most tasks; in Table we compare existing direct reaction prediction implementations against Chemformer and Chemformer-Large, fine-tuned for 150 and 100 epochs, respectively. Additionally, Table compares the Chemformer model, fine-tuned for 500 epochs, and the Chemformer-Large model, fine-tuned for 200 epochs, against existing SMILES-and graph-based approaches on the USPTO-50K retrosynthesis dataset. All Chemformer models were pre-trained on the combined pre-training task. From the results on the forward prediction datasets and the retrosynthesis prediction dataset we can see that both Chemformer model sizes are able to outperform the existing SMILES-based state-of-the-art on top-1 results. Chemformer-Large is also able to outperform the best graphbased models on top-1 predictions. However, the tables also show that existing methods predict significantly more reactions correctly for top-5 and top-10 evaluation. We examine this effect in more detail in Section 4.