Figure shows that the error on the pre-training set initially has a sharp drop as the model learns new reactions but gradually increases as the number of maximum fine-tuning epochs increases. The error on the experimental set also decreases at first but soon levels off at around 10 epochs. The result indicates that the chosen epoch maintains a good balance between retaining previously learnt knowledge and learning new data. At higher epochs, however, the model starts to lose prior knowledge without much added benefits as it becomes biased toward the three reaction families found in the fine-tuning set. It is thus important to identify optimal epochs and hyperparameters for the fine-tuned model if one seeks to preserve the pre-trained information.