Sequence transduction models can only be used for language translation tasks and cannot be applied to other types of sequence data such as time series or genomic sequences.

Sequence transduction models are designed to map one sequence to another, and this idea is not limited to language. They can handle any data that can be represented as a list of elements, such as sensor readings, stock prices, or nucleotides in DNA, because the models learn to predict the next part of a sequence from the previous parts. For example, a model could take a series of heart‑beat intervals and output the next interval, helping to forecast arrhythmias. Similarly, a model could read a DNA sequence and produce the corresponding protein sequence, just as it translates words from one language to another. Thus, these models are versatile tools that work well with many kinds of sequential data.