The DECIMER Image Transformer now includes a feature for extracting token-level confidence scores from the model's output. The final layer of the Transformer decoder generates a value for each token within the output vocabulary. These values are then normalised to sum up to 1 using a Softmax activation function. During each prediction step, the token associated with the highest value is selected, and the value itself is regarded as a measure of confidence. This process bears similarity to the methodology used for determining confidence scores in MolScribe, as previously described by Qian et al. . The token-level scores can then be averaged to yield one score for the whole predicted SMILES string. This confidence score allows us to determine how well the model can interpret a given hand-drawn chemical structure image (see Figure ).