You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: In addition to performance improvement, we show that SAT offers better model interpretability compared to the classic Transformer with only absolute positional encoding. We respectively train a SAT model and a Transformer with a CLS readout on the Mutagenicity dataset, and visualize the attention scores between the [CLS] node and other nodes learned by SAT and the Transformer in Figure 4. The salient difference between the two models is that SAT has structure-aware node embeddings, and thus we can attribute the following interpretability gains to that. While both models manage to identify some chemical motifs known for mutagenicity, such as NO{}_{2} and NH{}_{2}, the attention scores learned by SAT are sparser and more informative, meaning that SAT puts more attention weights on these known mutagenic motifs than the Transformer with RWPE. The vanilla Transformer even fails to put attention on some important atoms such as the H atoms in the NH{}_{2} group. The only H atoms highlighted by SAT are those in the NH{}_{2} group, suggesting that our SAT indeed takes the structure into account. More focus on these discriminative motifs makes the SAT model less influenced by other chemical patterns that commonly exist in the dataset, such as benzene, and thus leads to overall improved performance. More results are provided in the Appendix.

Question: Did author experiment only with Mutagenicity dataset to show the interpretability of proposed model?
No. Author conducted additional experiments for model interpretability in appendix.
The reason is that they mentioned it in section 5.5.