A second category of de novo sequencing models employs the transformer architecture (Figure ). Transformers were first developed for natural language processing, such as translating from English to German and English to French , but have since then found success in a broad range of application domains, including modeling DNA and protein sequences . The transformer can handle variable-length inputs and the model architecture is indifferent to the order of input tokens. Hence, it is often necessary to explicitly encode the position of each input token and provide these encoded positions along with the tokens themselves. Notably, using positional embeddings removes the need to discretize the m/z axis of mass spectra, avoiding the corresponding issues mentioned before.