We conducted large-scale training on a dataset of nearly one billion compounds to improve model generalisability. While we observed a slight decrease in accuracy on specific test datasets, the overall performance on a comprehensive benchmark dataset showed significant improvement. This suggests the increased training data enhanced the model's ability to handle a wider range of chemical structures. Moreover, a bigger model, such as the Large Language Model (LLM), could be used along with bigger training data.